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CARL-main/carl/envs/box2d/utils.py
from typing import List import Box2D def safe_destroy(world: Box2D.b2World, bodies: List[Box2D.b2Body]) -> None: for body in bodies: try: world.DestroyBody(body) except AssertionError as error: if str(error) != "m_bodyCount > 0": raise error
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CARL-main/carl/envs/box2d/parking_garage/__init__.py
__author__ = "André Biedenkapp"
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CARL-main/carl/envs/box2d/parking_garage/bus.py
from typing import List import Box2D import numpy as np from Box2D.b2 import circleShape # noqa: F401 from Box2D.b2 import contactListener # noqa: F401 from Box2D.b2 import distanceJointDef # noqa: F401 from Box2D.b2 import edgeShape # noqa: F401 from Box2D.b2 import fixtureDef # noqa: F401 from Box2D.b2 import polygonShape # noqa: F401 from Box2D.b2 import prismaticJointDef # noqa: F401 from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 from gym.envs.box2d.car_dynamics import Car from carl.envs.box2d.parking_garage.utils import Particle __author__ = "André Biedenkapp" """ Original Simulator parameters from gym.envs.box2d.car_dynamics.Car If we replace one value with some other we comment the value here and replace it below with our own values """ SIZE = 0.02 WHEEL_R = 27 WHEEL_COLOR = (0.0, 0.0, 0.0) WHEEL_WHITE = (0.3, 0.3, 0.3) MUD_COLOR = (0.4, 0.4, 0.0) """ Changed and added Simulator parameters """ ENGINE_POWER = 50_000_000 * SIZE * SIZE WHEEL_MOMENT_OF_INERTIA = 6_000 * SIZE * SIZE FRICTION_LIMIT = ( 1_250_000 * SIZE * SIZE ) # friction ~= mass ~= size^2 (calculated implicitly using density) MOTOR_WHEEL_COLOR = (0.6, 0.6, 0.8) WHEEL_W = 20 # Car Polys WHEELPOS = [(-85, +650), (+85, +650), (-85, -30), (+85, -30)] HULL_POLY1 = [(-70, +700), (+70, +700), (+80, +550), (-80, +550)] HULL_POLY2 = [(-90, +550), (+90, +550), (+90, -60), (-90, -60)] # Polys for small trailer STRAILER_POLY = [ (-15, -70), (+15, -70), (-60, -100), (+60, -100), (-60, -240), (+60, -240) # (-15, -130), (+15, -130), # (-60, -160), (+60, -160), # (-60, -300), (+60, -300) ] STRAILERWHEELPOS = [(-65, -170), (+65, -170)] # Polys for large trailer ATRAILER_POLY = [(-90, -80), (+90, -80), (-90, -110), (+90, -110)] ATRAILER_POLY2 = [ (-40, -80), (+40, -80), (-40, -140), (+40, -140), ] ATRAILER_POLY3 = [(-90, -140), (+90, -140), (-90, -640), (+90, -640)] ATRAILERWHEELPOS = [(-95, -95), (+95, -95), (-95, -605), (+95, -605)] class Bus(Car): """ Different body to the original OpenAI car. We also added a brake bias with 40% front and 60% rear break bias """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) def __init__( self, world: Box2D.b2World, init_angle: float, init_x: float, init_y: float ) -> None: self._init_extra_params() self.world = world ##### SETUP MAIN BODY #### # noqa: E266 self.hull = self.world.CreateDynamicBody( position=(init_x, init_y), angle=init_angle, fixtures=[ fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY1] ), density=0.66, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY2] ), density=0.2, ), ], ) self.hull.color = (1.0, 0.85, 0.0) self.wheels = [] self.fuel_spent = 0.0 WHEEL_POLY = [ (-WHEEL_W, +WHEEL_R), (+WHEEL_W, +WHEEL_R), (+WHEEL_W, -WHEEL_R), (-WHEEL_W, -WHEEL_R), ] for wx, wy in WHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE if wy > 0 and self.fwd: w.color = MOTOR_WHEEL_COLOR elif wy < 0 and self.rwd: w.color = MOTOR_WHEEL_COLOR else: w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.hull, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.7, upperAngle=+0.7, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) ##### SETUP SMALL TRAILER #### # noqa: E266 if self.trailer_type == 1: self.trailer = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in STRAILER_POLY] ), density=2.0, ), ) self.trailer.color = (0.0, 0.0, 0.8) rjd = revoluteJointDef( bodyA=self.hull, bodyB=self.trailer, localAnchorA=(0, -60 * SIZE), localAnchorB=(0, -70 * SIZE), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 100 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.9, upperAngle=+0.9, ) self.trailer.joint = self.world.CreateJoint(rjd) for wx, wy in STRAILERWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.trailer, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) self.drawlist = self.wheels + [self.hull, self.trailer] ##### SETUP LARGE TRAILER #### # noqa: E266 elif self.trailer_type == 2: self.trailer_axel = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY] ), density=0.5, categoryBits=0x0020, maskBits=0x001, ), ) self.trailer = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=[ fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY2] ), density=5.0, categoryBits=0x0020, maskBits=0x001, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY3] ), density=1.5, categoryBits=0x0020, maskBits=0x001, ), ], ) rjd = distanceJointDef( bodyA=self.hull, bodyB=self.trailer_axel, localAnchorA=(0, -60 * SIZE), localAnchorB=(-7.5 * SIZE, -80 * SIZE), dampingRatio=0, frequencyHz=500, length=1.25, ) self.trailer_axel.joint = self.world.CreateJoint(rjd) rjd = distanceJointDef( bodyA=self.hull, bodyB=self.trailer_axel, localAnchorA=(0, -60 * SIZE), localAnchorB=(+7.5 * SIZE, -80 * SIZE), dampingRatio=0, frequencyHz=500, length=1.25, ) self.trailer_axel.joint = self.world.CreateJoint(rjd) self.trailer_axel.color = (0.0, 0.8, 0.8) rjd = revoluteJointDef( bodyA=self.trailer_axel, bodyB=self.trailer, localAnchorA=(0.0, -95 * SIZE), localAnchorB=(0, -95 * SIZE), enableMotor=True, enableLimit=True, maxMotorTorque=360 * 3000 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.5, upperAngle=+0.5, ) self.trailer.color = (0.0, 0.0, 0.8) self.trailer.joint = self.world.CreateJoint(rjd) for wx, wy in ATRAILERWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.trailer if wy < -170 else self.trailer_axel, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) self.drawlist = self.wheels + [self.hull, self.trailer, self.trailer_axel] else: self.drawlist = self.wheels + [self.hull] self.particles: List[Particle] = [] def gas(self, gas: float) -> None: """control: rear wheel drive Args: gas (float): How much gas gets applied. Gets clipped between 0 and 1. """ gas = np.clip(gas, 0, 1) if self.fwd: for w in self.wheels[:2]: diff = gas - w.gas if diff > 0.1: diff = 0.1 # gradually increase, but stop immediately w.gas += diff if self.rwd: for w in self.wheels[2:4]: diff = gas - w.gas if diff > 0.1: diff = 0.1 # gradually increase, but stop immediately w.gas += diff def brake(self, b: float) -> None: """control: brake Args: b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" for w in self.wheels[:2]: w.brake = b * 0.4 for w in self.wheels[2:4]: w.brake = b * 0.6 if self.trailer_type == 1: for w in self.wheels[4:6]: w.brake = b * 0.7 if self.trailer_type == 2: for w in self.wheels[4:]: w.brake = b * 0.8 def steer(self, s: float) -> None: """control: steer Args: s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" self.wheels[0].steer = s self.wheels[1].steer = s def step(self, dt: float) -> None: """ Copy of the original step function of 'gym.envs.box2d.car_dynamics.Car' needed to accept different Engin powers or other fixed parameters dt : float Timestep for simulation """ for w in self.wheels: # Steer each wheel dir = np.sign(w.steer - w.joint.angle) val = abs(w.steer - w.joint.angle) w.joint.motorSpeed = dir * min(50.0 * val, 3.0) # Position => friction_limit grass = True friction_limit = FRICTION_LIMIT * 0.6 # Grass friction if no tile for tile in w.tiles: friction_limit = max( friction_limit, FRICTION_LIMIT * tile.road_friction ) grass = False # Force forw = w.GetWorldVector((0, 1)) side = w.GetWorldVector((1, 0)) v = w.linearVelocity vf = forw[0] * v[0] + forw[1] * v[1] # forward speed vs = side[0] * v[0] + side[1] * v[1] # side speed # WHEEL_MOMENT_OF_INERTIA*np.square(w.omega)/2 = E -- energy # WHEEL_MOMENT_OF_INERTIA*w.omega * domega/dt = dE/dt = W -- power # domega = dt*W/WHEEL_MOMENT_OF_INERTIA/w.omega # add small coef not to divide by zero w.omega += ( dt * ENGINE_POWER * w.gas / WHEEL_MOMENT_OF_INERTIA / (abs(w.omega) + 5.0) ) self.fuel_spent += dt * ENGINE_POWER * w.gas if w.brake >= 0.9: w.omega = 0 elif w.brake > 0: BRAKE_FORCE = 15 # radians per second dir = -np.sign(w.omega) val = BRAKE_FORCE * w.brake if abs(val) > abs(w.omega): val = abs(w.omega) # low speed => same as = 0 w.omega += dir * val w.phase += w.omega * dt vr = w.omega * w.wheel_rad # rotating wheel speed f_force = -vf + vr # force direction is direction of speed difference p_force = -vs # Physically correct is to always apply friction_limit until speed is equal. # But dt is finite, that will lead to oscillations if difference is already near zero. # Random coefficient to cut oscillations in few steps (have no effect on friction_limit) f_force *= 205000 * SIZE * SIZE p_force *= 205000 * SIZE * SIZE force = np.sqrt(np.square(f_force) + np.square(p_force)) # Skid trace if abs(force) > 2.0 * friction_limit: if ( w.skid_particle and w.skid_particle.grass == grass and len(w.skid_particle.poly) < 30 ): w.skid_particle.poly.append((w.position[0], w.position[1])) elif w.skid_start is None: w.skid_start = w.position else: w.skid_particle = self._create_particle( w.skid_start, w.position, grass ) w.skid_start = None else: w.skid_start = None w.skid_particle = None if abs(force) > friction_limit: f_force /= force p_force /= force force = friction_limit # Correct physics here f_force *= force p_force *= force w.omega -= dt * f_force * w.wheel_rad / WHEEL_MOMENT_OF_INERTIA w.ApplyForceToCenter( ( p_force * side[0] + f_force * forw[0], p_force * side[1] + f_force * forw[1], ), True, ) class FWDBus(Bus): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDBus(Bus): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class BusSmallTrailer(Bus): """ Bus with small trailer attached """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class FWDBusSmallTrailer(Bus): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDBusSmallTrailer(Bus): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class BusLargeTrailer(Bus): """ Bus with small trailer attached """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class FWDBusLargeTrailer(Bus): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDBusLargeTrailer(Bus): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large )
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CARL-main/carl/envs/box2d/parking_garage/race_car.py
from typing import List import Box2D import numpy as np from Box2D.b2 import circleShape # noqa: F401 from Box2D.b2 import contactListener # noqa: F401 from Box2D.b2 import distanceJointDef # noqa: F401 from Box2D.b2 import edgeShape # noqa: F401 from Box2D.b2 import fixtureDef # noqa: F401 from Box2D.b2 import polygonShape # noqa: F401 from Box2D.b2 import prismaticJointDef # noqa: F401 from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 from gym.envs.box2d.car_dynamics import Car from carl.envs.box2d.parking_garage.utils import Particle __author__ = "André Biedenkapp" """ Original Simulator parameters from gym.envs.box2d.car_dynamics.Car If we replace one value with some other we comment the value here and replace it below with our own values """ SIZE = 0.02 ENGINE_POWER = 100_000_000 * SIZE * SIZE WHEEL_MOMENT_OF_INERTIA = 4_000 * SIZE * SIZE FRICTION_LIMIT = ( 1_000_000 * SIZE * SIZE ) # friction ~= mass ~= size^2 (calculated implicitly using density) WHEEL_R = 27 WHEEL_W = 14 WHEELPOS = [(-55, +80), (+55, +80), (-55, -82), (+55, -82)] HULL_POLY1 = [(-60, +130), (+60, +130), (+60, +110), (-60, +110)] HULL_POLY2 = [(-15, +120), (+15, +120), (+20, +20), (-20, 20)] HULL_POLY3 = [ (+25, +20), (+50, -10), (+50, -40), (+20, -90), (-20, -90), (-50, -40), (-50, -10), (-25, +20), ] HULL_POLY4 = [(-50, -120), (+50, -120), (+50, -90), (-50, -90)] WHEEL_COLOR = (0.0, 0.0, 0.0) WHEEL_WHITE = (0.3, 0.3, 0.3) MUD_COLOR = (0.4, 0.4, 0.0) """ Changed and added Simulator parameters """ MOTOR_WHEEL_COLOR = (0.6, 0.6, 0.8) # Polys for small trailer STRAILER_POLY = [ (-15, -130), (+15, -130), (-60, -160), (+60, -160), (-60, -300), (+60, -300), ] STRAILERWHEELPOS = [(-65, -230), (+65, -230)] # Polys for large trailer ATRAILER_POLY = [(-60, -140), (+60, -140), (-60, -170), (+60, -170)] ATRAILER_POLY2 = [ (-10, -140), (+10, -140), (-10, -200), (+10, -200), ] ATRAILER_POLY3 = [(-60, -200), (+60, -200), (-60, -500), (+60, -500)] ATRAILERWHEELPOS = [(-65, -155), (+65, -155), (-65, -485), (+65, -485)] class RaceCar(Car): """ The default race car does only differ from the original race car in openAI gym by changing the color of the driven wheels. We also added a brake bias with 40% front and 60% rear break bias """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) def __init__( self, world: Box2D.b2World, init_angle: float, init_x: float, init_y: float ) -> None: self._init_extra_params() self.world = world ##### SETUP MAIN BODY #### # noqa: E266 self.hull = self.world.CreateDynamicBody( position=(init_x, init_y), angle=init_angle, fixtures=[ fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY1] ), density=1.0, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY2] ), density=1.0, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY3] ), density=1.0, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY4] ), density=1.0, ), ], ) self.hull.color = (0.8, 0.0, 0.0) self.wheels = [] self.fuel_spent = 0.0 WHEEL_POLY = [ (-WHEEL_W, +WHEEL_R), (+WHEEL_W, +WHEEL_R), (+WHEEL_W, -WHEEL_R), (-WHEEL_W, -WHEEL_R), ] for wx, wy in WHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE if wy > 0 and self.fwd: w.color = MOTOR_WHEEL_COLOR elif wy < 0 and self.rwd: w.color = MOTOR_WHEEL_COLOR else: w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.hull, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) ##### SETUP SMALL TRAILER #### # noqa: E266 if self.trailer_type == 1: self.trailer = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in STRAILER_POLY] ), density=0.75, ), ) self.trailer.color = (0.0, 0.0, 0.8) rjd = revoluteJointDef( bodyA=self.hull, bodyB=self.trailer, localAnchorA=(0, -120 * SIZE), localAnchorB=(0, -135 * SIZE), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.9, upperAngle=+0.9, ) self.trailer.joint = self.world.CreateJoint(rjd) for wx, wy in STRAILERWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.trailer, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) self.drawlist = self.wheels + [self.hull, self.trailer] ##### SETUP LARGE TRAILER #### # noqa: E266 elif self.trailer_type == 2: self.trailer_axel = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY] ), density=0.5, categoryBits=0x0020, maskBits=0x001, ), ) self.trailer = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=[ fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY2] ), density=5.0, categoryBits=0x0020, maskBits=0x001, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY3] ), density=0.5, categoryBits=0x0020, maskBits=0x001, ), ], ) rjd = distanceJointDef( bodyA=self.hull, bodyB=self.trailer_axel, localAnchorA=(0, -120 * SIZE), localAnchorB=(-7.5 * SIZE, -140 * SIZE), dampingRatio=0, frequencyHz=500, length=1.25, ) self.trailer_axel.joint = self.world.CreateJoint(rjd) rjd = distanceJointDef( bodyA=self.hull, bodyB=self.trailer_axel, localAnchorA=(0, -120 * SIZE), localAnchorB=(+7.5 * SIZE, -140 * SIZE), dampingRatio=0, frequencyHz=500, length=1.25, ) self.trailer_axel.joint = self.world.CreateJoint(rjd) self.trailer_axel.color = (0.0, 0.8, 0.8) rjd = revoluteJointDef( bodyA=self.trailer_axel, bodyB=self.trailer, localAnchorA=(0.0, -155 * SIZE), localAnchorB=(0, -155 * SIZE), enableMotor=True, enableLimit=True, maxMotorTorque=360 * 3000 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.5, upperAngle=+0.5, ) self.trailer.color = (0.0, 0.0, 0.8) self.trailer.joint = self.world.CreateJoint(rjd) for wx, wy in ATRAILERWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.trailer if wy < -170 else self.trailer_axel, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) self.drawlist = self.wheels + [self.hull, self.trailer, self.trailer_axel] else: self.drawlist = self.wheels + [self.hull] self.particles: List[Particle] = [] def gas(self, gas: float) -> None: """control: rear wheel drive Args: gas (float): How much gas gets applied. Gets clipped between 0 and 1. """ gas = np.clip(gas, 0, 1) if self.fwd: for w in self.wheels[:2]: diff = gas - w.gas if diff > 0.1: diff = 0.1 # gradually increase, but stop immediately w.gas += diff if self.rwd: for w in self.wheels[2:4]: diff = gas - w.gas if diff > 0.1: diff = 0.1 # gradually increase, but stop immediately w.gas += diff def brake(self, b: float) -> None: """control: brake Args: b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" for w in self.wheels[:2]: w.brake = b * 0.4 for w in self.wheels[2:4]: w.brake = b * 0.6 if self.trailer_type == 1: for w in self.wheels[4:6]: w.brake = b * 0.7 if self.trailer_type == 2: for w in self.wheels[4:]: w.brake = b * 0.8 def steer(self, s: float) -> None: """control: steer Args: s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" self.wheels[0].steer = s self.wheels[1].steer = s def step(self, dt: float) -> None: """ Copy of the original step function of 'gym.envs.box2d.car_dynamics.Car' needed to accept different engine powers or other fixed parameters dt : float Timestep for simulation """ for w in self.wheels: # Steer each wheel dir = np.sign(w.steer - w.joint.angle) val = abs(w.steer - w.joint.angle) w.joint.motorSpeed = dir * min(50.0 * val, 3.0) # Position => friction_limit grass = True friction_limit = FRICTION_LIMIT * 0.6 # Grass friction if no tile for tile in w.tiles: friction_limit = max( friction_limit, FRICTION_LIMIT * tile.road_friction ) grass = False # Force forw = w.GetWorldVector((0, 1)) side = w.GetWorldVector((1, 0)) v = w.linearVelocity vf = forw[0] * v[0] + forw[1] * v[1] # forward speed vs = side[0] * v[0] + side[1] * v[1] # side speed # WHEEL_MOMENT_OF_INERTIA*np.square(w.omega)/2 = E -- energy # WHEEL_MOMENT_OF_INERTIA*w.omega * domega/dt = dE/dt = W -- power # domega = dt*W/WHEEL_MOMENT_OF_INERTIA/w.omega # add small coef not to divide by zero w.omega += ( dt * ENGINE_POWER * w.gas / WHEEL_MOMENT_OF_INERTIA / (abs(w.omega) + 5.0) ) self.fuel_spent += dt * ENGINE_POWER * w.gas if w.brake >= 0.9: w.omega = 0 elif w.brake > 0: BRAKE_FORCE = 15 # radians per second dir = -np.sign(w.omega) val = BRAKE_FORCE * w.brake if abs(val) > abs(w.omega): val = abs(w.omega) # low speed => same as = 0 w.omega += dir * val w.phase += w.omega * dt vr = w.omega * w.wheel_rad # rotating wheel speed f_force = -vf + vr # force direction is direction of speed difference p_force = -vs # Physically correct is to always apply friction_limit until speed is equal. # But dt is finite, that will lead to oscillations if difference is already near zero. # Random coefficient to cut oscillations in few steps (have no effect on friction_limit) f_force *= 205000 * SIZE * SIZE p_force *= 205000 * SIZE * SIZE force = np.sqrt(np.square(f_force) + np.square(p_force)) # Skid trace if abs(force) > 2.0 * friction_limit: if ( w.skid_particle and w.skid_particle.grass == grass and len(w.skid_particle.poly) < 30 ): w.skid_particle.poly.append((w.position[0], w.position[1])) elif w.skid_start is None: w.skid_start = w.position else: w.skid_particle = self._create_particle( w.skid_start, w.position, grass ) w.skid_start = None else: w.skid_start = None w.skid_particle = None if abs(force) > friction_limit: f_force /= force p_force /= force force = friction_limit # Correct physics here f_force *= force p_force *= force w.omega -= dt * f_force * w.wheel_rad / WHEEL_MOMENT_OF_INERTIA w.ApplyForceToCenter( ( p_force * side[0] + f_force * forw[0], p_force * side[1] + f_force * forw[1], ), True, ) class FWDRaceCar(RaceCar): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDRaceCar(RaceCar): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class RaceCarSmallTrailer(RaceCar): """ RaceCar with small trailer attached """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class FWDRaceCarSmallTrailer(RaceCar): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDRaceCarSmallTrailer(RaceCar): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class RaceCarLargeTrailer(RaceCar): """ RaceCar with small trailer attached """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class FWDRaceCarLargeTrailer(RaceCar): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDRaceCarLargeTrailer(RaceCar): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large )
22,179
34.374801
118
py
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CARL-main/carl/envs/box2d/parking_garage/street_car.py
from __future__ import annotations import Box2D import numpy as np from Box2D.b2 import circleShape # noqa: F401 from Box2D.b2 import contactListener # noqa: F401 from Box2D.b2 import distanceJointDef # noqa: F401 from Box2D.b2 import edgeShape # noqa: F401 from Box2D.b2 import fixtureDef # noqa: F401 from Box2D.b2 import polygonShape # noqa: F401 from Box2D.b2 import prismaticJointDef # noqa: F401 from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 from gym.envs.box2d.car_dynamics import Car __author__ = "André Biedenkapp" """ Original Simulator parameters from gym.envs.box2d.car_dynamics.Car If we replace one value with some other we comment the value here and replace it below with our own values """ SIZE = 0.02 WHEEL_R = 27 WHEEL_COLOR = (0.0, 0.0, 0.0) WHEEL_WHITE = (0.3, 0.3, 0.3) MUD_COLOR = (0.4, 0.4, 0.0) """ Changed and added Simulator parameters """ ENGINE_POWER = 10_000_000 * SIZE * SIZE WHEEL_MOMENT_OF_INERTIA = 1_000 * SIZE * SIZE FRICTION_LIMIT = ( 750_000 * SIZE * SIZE ) # friction ~= mass ~= size^2 (calculated implicitly using density) MOTOR_WHEEL_COLOR = (0.6, 0.6, 0.8) WHEEL_W = 10 # Car Polys WHEELPOS = [(-60, +140), (+60, +140), (-60, -30), (+60, -30)] HULL_POLY1 = [(-40, +180), (+40, +180), (+55, +160), (-55, +160)] HULL_POLY2 = [(-55, +160), (+55, +160), (+55, -60), (-55, -60)] HULL_POLY3 = [ (+55, -40), (-55, -40), (-65, -75), (+65, -75), ] # Polys for small trailer STRAILER_POLY = [ (-15, -85), (+15, -85), (-60, -115), (+60, -115), (-60, -255), (+60, -255) # (-15, -130), (+15, -130), # (-60, -160), (+60, -160), # (-60, -300), (+60, -300) ] STRAILERWHEELPOS = [(-65, -185), (+65, -185)] # Polys for large trailer ATRAILER_POLY = [(-60, -95), (+60, -95), (-60, -125), (+60, -125)] ATRAILER_POLY2 = [ (-10, -95), (+10, -95), (-10, -155), (+10, -155), ] ATRAILER_POLY3 = [(-60, -155), (+60, -155), (-60, -455), (+60, -455)] ATRAILERWHEELPOS = [(-65, -110), (+65, -110), (-65, -420), (+65, -420)] class StreetCar(Car): """ Different body to the original OpenAI car. We also added a brake bias with 40% front and 60% rear break bias """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) def __init__( self, world: Box2D.b2World, init_angle: float, init_x: int, init_y: int ): self._init_extra_params() self.world = world ##### SETUP MAIN BODY #### # noqa: E266 self.hull = self.world.CreateDynamicBody( position=(init_x, init_y), angle=init_angle, fixtures=[ fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY1] ), density=1.0, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY2] ), density=1.0, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in HULL_POLY3] ), density=1.0, ), ], ) self.hull.color = (1.0, 0.31, 0.0) self.wheels = [] self.fuel_spent = 0.0 WHEEL_POLY = [ (-WHEEL_W, +WHEEL_R), (+WHEEL_W, +WHEEL_R), (+WHEEL_W, -WHEEL_R), (-WHEEL_W, -WHEEL_R), ] for wx, wy in WHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE if wy > 0 and self.fwd: w.color = MOTOR_WHEEL_COLOR elif wy < 0 and self.rwd: w.color = MOTOR_WHEEL_COLOR else: w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.hull, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) ##### SETUP SMALL TRAILER #### # noqa: E266 if self.trailer_type == 1: self.trailer = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in STRAILER_POLY] ), density=0.75, ), ) self.trailer.color = (0.0, 0.0, 0.8) rjd = revoluteJointDef( bodyA=self.hull, bodyB=self.trailer, localAnchorA=(0, -75 * SIZE), localAnchorB=(0, -85 * SIZE), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.9, upperAngle=+0.9, ) self.trailer.joint = self.world.CreateJoint(rjd) for wx, wy in STRAILERWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.trailer, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) self.drawlist = self.wheels + [self.hull, self.trailer] ##### SETUP LARGE TRAILER #### # noqa: E266 elif self.trailer_type == 2: self.trailer_axel = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY] ), density=0.5, categoryBits=0x0020, maskBits=0x001, ), ) self.trailer = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=[ fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY2] ), density=5.0, categoryBits=0x0020, maskBits=0x001, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in ATRAILER_POLY3] ), density=0.5, categoryBits=0x0020, maskBits=0x001, ), ], ) rjd = distanceJointDef( bodyA=self.hull, bodyB=self.trailer_axel, localAnchorA=(0, -75 * SIZE), localAnchorB=(-7.5 * SIZE, -95 * SIZE), dampingRatio=0, frequencyHz=500, length=1.25, ) self.trailer_axel.joint = self.world.CreateJoint(rjd) rjd = distanceJointDef( bodyA=self.hull, bodyB=self.trailer_axel, localAnchorA=(0, -75 * SIZE), localAnchorB=(+7.5 * SIZE, -95 * SIZE), dampingRatio=0, frequencyHz=500, length=1.25, ) self.trailer_axel.joint = self.world.CreateJoint(rjd) self.trailer_axel.color = (0.0, 0.8, 0.8) rjd = revoluteJointDef( bodyA=self.trailer_axel, bodyB=self.trailer, localAnchorA=(0.0, -110 * SIZE), localAnchorB=(0, -110 * SIZE), enableMotor=True, enableLimit=True, maxMotorTorque=360 * 3000 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.5, upperAngle=+0.5, ) self.trailer.color = (0.0, 0.0, 0.8) self.trailer.joint = self.world.CreateJoint(rjd) for wx, wy in ATRAILERWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.trailer if wy < -170 else self.trailer_axel, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) self.drawlist = self.wheels + [self.hull, self.trailer, self.trailer_axel] else: self.drawlist = self.wheels + [self.hull] self.particles: list = [] def gas(self, gas: float) -> None: """control: rear wheel drive Args: gas (float): How much gas gets applied. Gets clipped between 0 and 1. """ gas = np.clip(gas, 0, 1) if self.fwd: for w in self.wheels[:2]: diff = gas - w.gas if diff > 0.1: diff = 0.1 # gradually increase, but stop immediately w.gas += diff if self.rwd: for w in self.wheels[2:4]: diff = gas - w.gas if diff > 0.1: diff = 0.1 # gradually increase, but stop immediately w.gas += diff def brake(self, b: float) -> None: """control: brake Args: b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" for w in self.wheels[:2]: w.brake = b * 0.4 for w in self.wheels[2:4]: w.brake = b * 0.6 if self.trailer_type == 1: for w in self.wheels[4:6]: w.brake = b * 0.7 if self.trailer_type == 2: for w in self.wheels[4:]: w.brake = b * 0.8 def steer(self, s: float) -> None: """control: steer Args: s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" self.wheels[0].steer = s self.wheels[1].steer = s def step(self, dt: float) -> None: """ Copy of the original step function of 'gym.envs.box2d.car_dynamics.Car' needed to accept different Engin powers or other fixed parameters :param dt: :return: """ for w in self.wheels: # Steer each wheel dir = np.sign(w.steer - w.joint.angle) val = abs(w.steer - w.joint.angle) w.joint.motorSpeed = dir * min(50.0 * val, 3.0) # Position => friction_limit grass = True friction_limit = FRICTION_LIMIT * 0.6 # Grass friction if no tile for tile in w.tiles: friction_limit = max( friction_limit, FRICTION_LIMIT * tile.road_friction ) grass = False # Force forw = w.GetWorldVector((0, 1)) side = w.GetWorldVector((1, 0)) v = w.linearVelocity vf = forw[0] * v[0] + forw[1] * v[1] # forward speed vs = side[0] * v[0] + side[1] * v[1] # side speed # WHEEL_MOMENT_OF_INERTIA*np.square(w.omega)/2 = E -- energy # WHEEL_MOMENT_OF_INERTIA*w.omega * domega/dt = dE/dt = W -- power # domega = dt*W/WHEEL_MOMENT_OF_INERTIA/w.omega # add small coef not to divide by zero w.omega += ( dt * ENGINE_POWER * w.gas / WHEEL_MOMENT_OF_INERTIA / (abs(w.omega) + 5.0) ) self.fuel_spent += dt * ENGINE_POWER * w.gas if w.brake >= 0.9: w.omega = 0 elif w.brake > 0: BRAKE_FORCE = 15 # radians per second dir = -np.sign(w.omega) val = BRAKE_FORCE * w.brake if abs(val) > abs(w.omega): val = abs(w.omega) # low speed => same as = 0 w.omega += dir * val w.phase += w.omega * dt vr = w.omega * w.wheel_rad # rotating wheel speed f_force = -vf + vr # force direction is direction of speed difference p_force = -vs # Physically correct is to always apply friction_limit until speed is equal. # But dt is finite, that will lead to oscillations if difference is already near zero. # Random coefficient to cut oscillations in few steps (have no effect on friction_limit) f_force *= 205000 * SIZE * SIZE p_force *= 205000 * SIZE * SIZE force = np.sqrt(np.square(f_force) + np.square(p_force)) # Skid trace if abs(force) > 2.0 * friction_limit: if ( w.skid_particle and w.skid_particle.grass == grass and len(w.skid_particle.poly) < 30 ): w.skid_particle.poly.append((w.position[0], w.position[1])) elif w.skid_start is None: w.skid_start = w.position else: w.skid_particle = self._create_particle( w.skid_start, w.position, grass ) w.skid_start = None else: w.skid_start = None w.skid_particle = None if abs(force) > friction_limit: f_force /= force p_force /= force force = friction_limit # Correct physics here f_force *= force p_force *= force w.omega -= dt * f_force * w.wheel_rad / WHEEL_MOMENT_OF_INERTIA w.ApplyForceToCenter( ( p_force * side[0] + f_force * forw[0], p_force * side[1] + f_force * forw[1], ), True, ) class FWDStreetCar(StreetCar): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDStreetCar(StreetCar): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class StreetCarSmallTrailer(StreetCar): """ StreetCar with small trailer attached """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class FWDStreetCarSmallTrailer(StreetCar): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDStreetCarSmallTrailer(StreetCar): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class StreetCarLargeTrailer(StreetCar): """ StreetCar with small trailer attached """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class FWDStreetCarLargeTrailer(StreetCar): """ Front wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = False # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large ) class AWDStreetCarLargeTrailer(StreetCar): """ 4x4 wheel driven race car """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = True # Flag to determine which wheels are driven self.trailer_type = ( 2 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large )
21,788
34.314425
113
py
null
CARL-main/carl/envs/box2d/parking_garage/trike.py
from typing import List import Box2D import numpy as np from Box2D.b2 import circleShape # noqa: F401 from Box2D.b2 import contactListener # noqa: F401 from Box2D.b2 import distanceJointDef # noqa: F401 from Box2D.b2 import edgeShape # noqa: F401 from Box2D.b2 import fixtureDef # noqa: F401 from Box2D.b2 import polygonShape # noqa: F401 from Box2D.b2 import prismaticJointDef # noqa: F401 from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 from gym.envs.box2d.car_dynamics import Car from carl.envs.box2d.parking_garage.utils import Particle __author__ = "André Biedenkapp" """ Original Simulator parameters from gym.envs.box2d.car_dynamics.Car If we replace one value with some other we comment the value here and replace it below with our own values """ SIZE = 0.02 WHEEL_COLOR = (0.0, 0.0, 0.0) WHEEL_WHITE = (0.3, 0.3, 0.3) MUD_COLOR = (0.4, 0.4, 0.0) """ Changed and added Simulator parameters """ MOTOR_WHEEL_COLOR = (0.6, 0.6, 0.8) ENGINE_POWER = 25_000_000 * SIZE * SIZE WHEEL_MOMENT_OF_INERTIA = 2_000 * SIZE * SIZE FRICTION_LIMIT = ( 1_000_000 * SIZE * SIZE ) # friction ~= mass ~= size^2 (calculated implicitly using density) WHEEL_R = 30 WHEEL_W = 10 BWHEELPOS = [(-0, +80), (-70, -82), (+70, -82)] BHULL_POLY2 = [(-15, +70), (+15, +70), (+20, +50), (-20, 50)] BHULL_POLY3 = [(-65, -120), (+65, -120), (+45, +50), (-45, +50)] # Polys for small trailer STRAILER_POLY = [ (-15, -130), (+15, -130), (-60, -160), (+60, -160), (-60, -300), (+60, -300), ] STRAILERWHEELPOS = [(-65, -230), (+65, -230)] class TukTuk(Car): """ TukTuk """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven (Not supported) self.trailer_type = 0 # Determines which trailer to attach 0 -> none, 1 -> small, 2 -> large (not supported) def __init__( self, world: Box2D.b2World, init_angle: float, init_x: float, init_y: float ) -> None: self._init_extra_params() self.world = world ##### SETUP MAIN BODY #### # noqa: E266 self.hull = self.world.CreateDynamicBody( position=(init_x, init_y), angle=init_angle, fixtures=[ fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in BHULL_POLY2] ), density=1, ), fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in BHULL_POLY3] ), density=1, ), ], ) self.hull.color = (0.2, 0.5, 1.0) self.wheels = [] self.fuel_spent = 0.0 WHEEL_POLY = [ (-WHEEL_W, +WHEEL_R), (+WHEEL_W, +WHEEL_R), (+WHEEL_W, -WHEEL_R), (-WHEEL_W, -WHEEL_R), ] for wx, wy in BWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE if wy > 0 and self.fwd: w.color = MOTOR_WHEEL_COLOR elif wy < 0 and self.rwd: w.color = MOTOR_WHEEL_COLOR else: w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.hull, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.9, upperAngle=+0.9, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) ##### SETUP SMALL TRAILER #### # noqa: E266 if self.trailer_type == 1: self.trailer = self.world.CreateDynamicBody( angle=init_angle, position=(init_x, init_y), fixtures=fixtureDef( shape=polygonShape( vertices=[(x * SIZE, y * SIZE) for x, y in STRAILER_POLY] ), density=0.75, ), ) self.trailer.color = (0.0, 0.0, 0.8) rjd = revoluteJointDef( bodyA=self.hull, bodyB=self.trailer, localAnchorA=(0, -120 * SIZE), localAnchorB=(0, -135 * SIZE), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.9, upperAngle=+0.9, ) self.trailer.joint = self.world.CreateJoint(rjd) for wx, wy in STRAILERWHEELPOS: front_k = 1.0 if wy > 0 else 1.0 w = self.world.CreateDynamicBody( position=(init_x + wx * SIZE, init_y + wy * SIZE), angle=init_angle, fixtures=fixtureDef( shape=polygonShape( vertices=[ (x * front_k * SIZE, y * front_k * SIZE) for x, y in WHEEL_POLY ] ), density=0.1, categoryBits=0x0020, maskBits=0x001, restitution=0.0, ), ) w.wheel_rad = front_k * WHEEL_R * SIZE w.color = WHEEL_COLOR w.gas = 0.0 w.brake = 0.0 w.steer = 0.0 w.phase = 0.0 # wheel angle w.omega = 0.0 # angular velocity w.skid_start = None w.skid_particle = None rjd = revoluteJointDef( bodyA=self.trailer, bodyB=w, localAnchorA=(wx * SIZE, wy * SIZE), localAnchorB=(0, 0), enableMotor=True, enableLimit=True, maxMotorTorque=180 * 900 * SIZE * SIZE, motorSpeed=0, lowerAngle=-0.4, upperAngle=+0.4, ) w.joint = self.world.CreateJoint(rjd) w.tiles = set() w.userData = w self.wheels.append(w) self.drawlist = self.wheels + [self.hull, self.trailer] ##### SETUP LARGE TRAILER #### # noqa: E266 elif self.trailer_type == 2: raise NotImplementedError else: self.drawlist = self.wheels + [self.hull] self.particles: List[Particle] = [] def steer(self, s: float) -> None: """control: steer Args: s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" self.wheels[0].steer = s def gas(self, gas: float) -> None: """control: rear wheel drive Args: gas (float): How much gas gets applied. Gets clipped between 0 and 1. """ gas = np.clip(gas, 0, 1) if self.fwd: raise NotImplementedError # Setting up FWD somehow causes an exception if self.rwd: for w in self.wheels[1:3]: diff = gas - w.gas if diff > 0.1: diff = 0.1 # gradually increase, but stop immediately w.gas += diff def brake(self, b: float) -> None: """control: brake Args: b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" for w in self.wheels[0]: w.brake = b * 10 for w in self.wheels[1:3]: w.brake = b * 0.6 if self.trailer_type == 1: for w in self.wheels[3:5]: w.brake = b * 0.8 if self.trailer_type == 2: for w in self.wheels[5:]: w.brake = b * 0.8 def step(self, dt: float) -> None: """ Copy of the original step function of 'gym.envs.box2d.car_dynamics.Car' needed to accept different Engin powers or other fixed parameters dt : float Timestep for simulation """ for w in self.wheels: # Steer each wheel dir = np.sign(w.steer - w.joint.angle) val = abs(w.steer - w.joint.angle) w.joint.motorSpeed = dir * min(50.0 * val, 3.0) # Position => friction_limit grass = True friction_limit = FRICTION_LIMIT * 0.6 # Grass friction if no tile for tile in w.tiles: friction_limit = max( friction_limit, FRICTION_LIMIT * tile.road_friction ) grass = False # Force forw = w.GetWorldVector((0, 1)) side = w.GetWorldVector((1, 0)) v = w.linearVelocity vf = forw[0] * v[0] + forw[1] * v[1] # forward speed vs = side[0] * v[0] + side[1] * v[1] # side speed # WHEEL_MOMENT_OF_INERTIA*np.square(w.omega)/2 = E -- energy # WHEEL_MOMENT_OF_INERTIA*w.omega * domega/dt = dE/dt = W -- power # domega = dt*W/WHEEL_MOMENT_OF_INERTIA/w.omega # add small coef not to divide by zero w.omega += ( dt * ENGINE_POWER * w.gas / WHEEL_MOMENT_OF_INERTIA / (abs(w.omega) + 5.0) ) self.fuel_spent += dt * ENGINE_POWER * w.gas if w.brake >= 0.9: w.omega = 0 elif w.brake > 0: BRAKE_FORCE = 15 # radians per second dir = -np.sign(w.omega) val = BRAKE_FORCE * w.brake if abs(val) > abs(w.omega): val = abs(w.omega) # low speed => same as = 0 w.omega += dir * val w.phase += w.omega * dt vr = w.omega * w.wheel_rad # rotating wheel speed f_force = -vf + vr # force direction is direction of speed difference p_force = -vs # Physically correct is to always apply friction_limit until speed is equal. # But dt is finite, that will lead to oscillations if difference is already near zero. # Random coefficient to cut oscillations in few steps (have no effect on friction_limit) f_force *= 205000 * SIZE * SIZE p_force *= 205000 * SIZE * SIZE force = np.sqrt(np.square(f_force) + np.square(p_force)) # Skid trace if abs(force) > 2.0 * friction_limit: if ( w.skid_particle and w.skid_particle.grass == grass and len(w.skid_particle.poly) < 30 ): w.skid_particle.poly.append((w.position[0], w.position[1])) elif w.skid_start is None: w.skid_start = w.position else: w.skid_particle = self._create_particle( w.skid_start, w.position, grass ) w.skid_start = None else: w.skid_start = None w.skid_particle = None if abs(force) > friction_limit: f_force /= force p_force /= force force = friction_limit # Correct physics here f_force *= force p_force *= force w.omega -= dt * f_force * w.wheel_rad / WHEEL_MOMENT_OF_INERTIA w.ApplyForceToCenter( ( p_force * side[0] + f_force * forw[0], p_force * side[1] + f_force * forw[1], ), True, ) class TukTukSmallTrailer(TukTuk): """ TukTuk with small trailer attached """ def _init_extra_params(self) -> None: self.rwd = True # Flag to determine which wheels are driven self.fwd = False # Flag to determine which wheels are driven self.trailer_type = ( 1 # Determines which trailer to attach 0 -> none, 1 -> small )
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null
CARL-main/carl/envs/box2d/parking_garage/utils.py
class Particle: pass
25
7.666667
15
py
null
CARL-main/carl/envs/brax/__init__.py
# flake8: noqa: F401 # Contexts and bounds by name from carl.envs.brax.carl_ant import CONTEXT_BOUNDS as CARLAnt_bounds from carl.envs.brax.carl_ant import DEFAULT_CONTEXT as CARLAnt_defaults from carl.envs.brax.carl_ant import CARLAnt from carl.envs.brax.carl_fetch import CONTEXT_BOUNDS as CARLFetch_bounds from carl.envs.brax.carl_fetch import DEFAULT_CONTEXT as CARLFetch_defaults from carl.envs.brax.carl_fetch import CARLFetch from carl.envs.brax.carl_grasp import CONTEXT_BOUNDS as CARLGrasp_bounds from carl.envs.brax.carl_grasp import DEFAULT_CONTEXT as CARLGrasp_defaults from carl.envs.brax.carl_grasp import CARLGrasp from carl.envs.brax.carl_halfcheetah import CONTEXT_BOUNDS as CARLHalfcheetah_bounds from carl.envs.brax.carl_halfcheetah import DEFAULT_CONTEXT as CARLHalfcheetah_defaults from carl.envs.brax.carl_halfcheetah import CARLHalfcheetah from carl.envs.brax.carl_humanoid import CONTEXT_BOUNDS as CARLHumanoid_bounds from carl.envs.brax.carl_humanoid import DEFAULT_CONTEXT as CARLHumanoid_defaults from carl.envs.brax.carl_humanoid import CARLHumanoid from carl.envs.brax.carl_ur5e import CONTEXT_BOUNDS as CARLUr5e_bounds from carl.envs.brax.carl_ur5e import DEFAULT_CONTEXT as CARLUr5e_defaults from carl.envs.brax.carl_ur5e import CARLUr5e
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py
null
CARL-main/carl/envs/brax/carl_ant.py
from typing import Any, Dict, List, Optional, Union import copy import json import brax import numpy as np from brax.envs.ant import _SYSTEM_CONFIG, Ant from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper from google.protobuf import json_format, text_format from google.protobuf.json_format import MessageToDict from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "joint_stiffness": 5000, "gravity": -9.8, "friction": 0.6, "angular_damping": -0.05, "actuator_strength": 300, "joint_angular_damping": 35, "torso_mass": 10, } CONTEXT_BOUNDS = { "joint_stiffness": (1, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), "angular_damping": (-np.inf, np.inf, float), "actuator_strength": (1, np.inf, float), "joint_angular_damping": (0, np.inf, float), "torso_mass": (0.1, np.inf, float), } class CARLAnt(CARLEnv): def __init__( self, env: Ant = Ant(), n_envs: int = 1, contexts: Contexts = {}, hide_context: bool = False, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if n_envs == 1: env = GymWrapper(env) else: env = VectorGymWrapper(VectorWrapper(env, n_envs)) self.base_config = MessageToDict( text_format.Parse(_SYSTEM_CONFIG, brax.Config()) ) if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, n_envs=n_envs, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: Ant config = copy.deepcopy(self.base_config) config["gravity"] = {"z": self.context["gravity"]} config["friction"] = self.context["friction"] config["angularDamping"] = self.context["angular_damping"] for j in range(len(config["joints"])): config["joints"][j]["angularDamping"] = self.context[ "joint_angular_damping" ] config["joints"][j]["stiffness"] = self.context["joint_stiffness"] for a in range(len(config["actuators"])): config["actuators"][a]["strength"] = self.context["actuator_strength"] config["bodies"][0]["mass"] = self.context["torso_mass"] # This converts the dict to a JSON String, then parses it into an empty brax config self.env.sys = brax.System( json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) ) def __getattr__(self, name: str) -> Any: if name in ["sys", "__getstate__"]: return getattr(self.env._environment, name) else: return getattr(self, name)
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CARL-main/carl/envs/brax/carl_fetch.py
from typing import Any, Dict, List, Optional, Union import copy import json import brax import numpy as np from brax.envs.fetch import _SYSTEM_CONFIG, Fetch from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper from google.protobuf import json_format, text_format from google.protobuf.json_format import MessageToDict from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "joint_stiffness": 5000, "gravity": -9.8, "friction": 0.6, "angular_damping": -0.05, # Angular velocity damping applied to each body "actuator_strength": 300, "joint_angular_damping": 35, # Damps parent and child angular velocities to be equal "torso_mass": 1, "target_radius": 2, "target_distance": 15, } CONTEXT_BOUNDS = { "joint_stiffness": (1, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), "angular_damping": (-np.inf, np.inf, float), "actuator_strength": (1, np.inf, float), "joint_angular_damping": (0, np.inf, float), "torso_mass": (0.1, np.inf, float), "target_radius": (0.1, np.inf, float), "target_distance": (0.1, np.inf, float), } class CARLFetch(CARLEnv): def __init__( self, env: Fetch = Fetch(), n_envs: int = 1, contexts: Contexts = {}, hide_context: bool = False, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if n_envs == 1: env = GymWrapper(env) else: env = VectorGymWrapper(VectorWrapper(env, n_envs)) self.base_config = MessageToDict( text_format.Parse(_SYSTEM_CONFIG, brax.Config()) ) if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, n_envs=n_envs, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: Fetch config = copy.deepcopy(self.base_config) config["gravity"] = {"z": self.context["gravity"]} config["friction"] = self.context["friction"] config["angularDamping"] = self.context["angular_damping"] for j in range(len(config["joints"])): config["joints"][j]["angularDamping"] = self.context[ "joint_angular_damping" ] config["joints"][j]["stiffness"] = self.context["joint_stiffness"] for a in range(len(config["actuators"])): config["actuators"][a]["strength"] = self.context["actuator_strength"] config["bodies"][0]["mass"] = self.context["torso_mass"] # This converts the dict to a JSON String, then parses it into an empty brax config self.env.sys = brax.System( json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) ) self.env.target_idx = self.env.sys.body.index["Target"] self.env.torso_idx = self.env.sys.body.index["Torso"] self.env.target_radius = self.context["target_radius"] self.env.target_distance = self.context["target_distance"] def __getattr__(self, name: str) -> Any: if name in [ "sys", "target_distance", "target_radius", "target_idx", "torso_idx", ]: return getattr(self.env._environment, name) else: return getattr(self, name)
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CARL-main/carl/envs/brax/carl_grasp.py
from typing import Any, Dict, List, Optional, Union import copy import json import brax import numpy as np from brax.envs.grasp import _SYSTEM_CONFIG, Grasp from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper from google.protobuf import json_format, text_format from google.protobuf.json_format import MessageToDict from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "joint_stiffness": 5000, "gravity": -9.8, "friction": 0.6, "angular_damping": -0.05, "actuator_strength": 300, "joint_angular_damping": 50, "target_radius": 1.1, "target_distance": 10.0, "target_height": 8.0, } CONTEXT_BOUNDS = { "joint_stiffness": (1, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), "angular_damping": (-np.inf, np.inf, float), "actuator_strength": (1, np.inf, float), "joint_angular_damping": (0, np.inf, float), "target_radius": (0.1, np.inf, float), "target_distance": (0.1, np.inf, float), "target_height": (0.1, np.inf, float), } class CARLGrasp(CARLEnv): def __init__( self, env: Grasp = Grasp(), n_envs: int = 1, contexts: Contexts = {}, hide_context: bool = False, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if n_envs == 1: env = GymWrapper(env) else: env = VectorGymWrapper(VectorWrapper(env, n_envs)) self.base_config = MessageToDict( text_format.Parse(_SYSTEM_CONFIG, brax.Config()) ) if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, n_envs=n_envs, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: Grasp config = copy.deepcopy(self.base_config) config["gravity"] = {"z": self.context["gravity"]} config["friction"] = self.context["friction"] config["angularDamping"] = self.context["angular_damping"] for j in range(len(config["joints"])): config["joints"][j]["angularDamping"] = self.context[ "joint_angular_damping" ] config["joints"][j]["stiffness"] = self.context["joint_stiffness"] for a in range(len(config["actuators"])): config["actuators"][a]["strength"] = self.context["actuator_strength"] # This converts the dict to a JSON String, then parses it into an empty brax config self.env.sys = brax.System( json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) ) self.env.object_idx = self.env.sys.body.index["Object"] self.env.target_idx = self.env.sys.body.index["Target"] self.env.hand_idx = self.env.sys.body.index["HandThumbProximal"] self.env.palm_idx = self.env.sys.body.index["HandPalm"] self.env.target_radius = self.context["target_radius"] self.env.target_distance = self.context["target_distance"] self.env.target_height = self.context["target_height"] def __getattr__(self, name: str) -> Any: if name in [ "sys", "object_idx", "target_idx", "hand_idx", "palm_idx", "target_radius", "target_distance", "target_height", ]: return getattr(self.env._environment, name) else: return getattr(self, name)
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CARL-main/carl/envs/brax/carl_halfcheetah.py
from typing import Any, Dict, List, Optional, Union import copy import json import brax import numpy as np from brax.envs.half_cheetah import _SYSTEM_CONFIG, Halfcheetah from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper from google.protobuf import json_format, text_format from google.protobuf.json_format import MessageToDict from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "joint_stiffness": 15000.0, "gravity": -9.8, "friction": 0.6, "angular_damping": -0.05, "joint_angular_damping": 20, "torso_mass": 9.457333, } CONTEXT_BOUNDS = { "joint_stiffness": (1, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), "angular_damping": (-np.inf, np.inf, float), "joint_angular_damping": (0, np.inf, float), "torso_mass": (0.1, np.inf, float), } class CARLHalfcheetah(CARLEnv): def __init__( self, env: Halfcheetah = Halfcheetah(), n_envs: int = 1, contexts: Contexts = {}, hide_context: bool = False, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if n_envs == 1: env = GymWrapper(env) else: env = VectorGymWrapper(VectorWrapper(env, n_envs)) self.base_config = MessageToDict( text_format.Parse(_SYSTEM_CONFIG, brax.Config()) ) if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, n_envs=n_envs, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: Halfcheetah config = copy.deepcopy(self.base_config) config["gravity"] = {"z": self.context["gravity"]} config["friction"] = self.context["friction"] config["angularDamping"] = self.context["angular_damping"] for j in range(len(config["joints"])): config["joints"][j]["angularDamping"] = self.context[ "joint_angular_damping" ] config["joints"][j]["stiffness"] = self.context["joint_stiffness"] config["bodies"][0]["mass"] = self.context["torso_mass"] # This converts the dict to a JSON String, then parses it into an empty brax config self.env.sys = brax.System( json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) ) def __getattr__(self, name: str) -> Any: if name in ["sys"]: return getattr(self.env._environment, name) else: return getattr(self, name)
3,994
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CARL-main/carl/envs/brax/carl_humanoid.py
from typing import Any, Dict, List, Optional, Union import copy import json import brax import numpy as np from brax import jumpy as jp from brax.envs.humanoid import _SYSTEM_CONFIG, Humanoid from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper from brax.physics import bodies from google.protobuf import json_format, text_format from google.protobuf.json_format import MessageToDict from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "gravity": -9.8, "friction": 0.6, "angular_damping": -0.05, "joint_angular_damping": 20, "torso_mass": 8.907463, } CONTEXT_BOUNDS = { "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), "angular_damping": (-np.inf, np.inf, float), "joint_angular_damping": (0, np.inf, float), "torso_mass": (0.1, np.inf, float), } class CARLHumanoid(CARLEnv): def __init__( self, env: Humanoid = Humanoid(), n_envs: int = 1, contexts: Contexts = {}, hide_context: bool = False, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if n_envs == 1: env = GymWrapper(env) else: env = VectorGymWrapper(VectorWrapper(env, n_envs)) self.base_config = MessageToDict( text_format.Parse(_SYSTEM_CONFIG, brax.Config()) ) if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, n_envs=n_envs, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: Humanoid config = copy.deepcopy(self.base_config) config["gravity"] = {"z": self.context["gravity"]} config["friction"] = self.context["friction"] config["angularDamping"] = self.context["angular_damping"] for j in range(len(config["joints"])): config["joints"][j]["angularDamping"] = self.context[ "joint_angular_damping" ] config["bodies"][0]["mass"] = self.context["torso_mass"] # This converts the dict to a JSON String, then parses it into an empty brax config protobuf_config = json_format.Parse( json.dumps(config, cls=NumpyEncoder), brax.Config() ) self.env.sys = brax.System(protobuf_config) body = bodies.Body(config=self.env.sys.config) body = jp.take(body, body.idx[:-1]) # skip the floor body self.env.mass = body.mass.reshape(-1, 1) self.env.inertia = body.inertia def __getattr__(self, name: str) -> Any: if name in ["sys", "body", "mass", "inertia"]: return getattr(self.env._environment, name) else: return getattr(self, name)
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CARL-main/carl/envs/brax/carl_ur5e.py
from typing import Any, Dict, List, Optional, Union import copy import json import brax import numpy as np from brax.envs.ur5e import _SYSTEM_CONFIG, Ur5e from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper from google.protobuf import json_format, text_format from google.protobuf.json_format import MessageToDict from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "joint_stiffness": 40000, "gravity": -9.81, "friction": 0.6, "angular_damping": -0.05, "actuator_strength": 100, "joint_angular_damping": 50, "target_radius": 0.02, "target_distance": 0.5, "torso_mass": 1.0, } CONTEXT_BOUNDS = { "joint_stiffness": (1, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), "angular_damping": (-np.inf, np.inf, float), "actuator_strength": (1, np.inf, float), "joint_angular_damping": (0, 360, float), "target_radius": (0.01, np.inf, float), "target_distance": (0.01, np.inf, float), "torso_mass": (0, np.inf, float), } class CARLUr5e(CARLEnv): def __init__( self, env: Ur5e = Ur5e(), n_envs: int = 1, contexts: Contexts = {}, hide_context: bool = False, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if n_envs == 1: env = GymWrapper(env) else: env = VectorGymWrapper(VectorWrapper(env, n_envs)) self.base_config = MessageToDict( text_format.Parse(_SYSTEM_CONFIG, brax.Config()) ) if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, n_envs=n_envs, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: Ur5e config = copy.deepcopy(self.base_config) config["gravity"] = {"z": self.context["gravity"]} config["friction"] = self.context["friction"] config["angularDamping"] = self.context["angular_damping"] for j in range(len(config["joints"])): config["joints"][j]["angularDamping"] = self.context[ "joint_angular_damping" ] config["joints"][j]["stiffness"] = self.context["joint_stiffness"] for a in range(len(config["actuators"])): config["actuators"][a]["strength"] = self.context["actuator_strength"] config["bodies"][0]["mass"] = self.context["torso_mass"] # This converts the dict to a JSON String, then parses it into an empty brax config self.env.sys = brax.System( json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) ) self.env.target_idx = self.env.sys.body.index["Target"] self.env.torso_idx = self.env.sys.body.index["wrist_3_link"] self.env.target_radius = self.context["target_radius"] self.env.target_distance = self.context["target_distance"] def __getattr__(self, name: str) -> Any: if name in [ "sys", "target_idx", "torso_idx", "target_radius", "target_distance", ]: return getattr(self.env._environment, name) else: return getattr(self, name)
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CARL-main/carl/envs/classic_control/__init__.py
# flake8: noqa: F401 # Contexts and bounds by name from carl.envs.classic_control.carl_acrobot import ( CONTEXT_BOUNDS as CARLAcrobotEnv_bounds, ) from carl.envs.classic_control.carl_acrobot import ( DEFAULT_CONTEXT as CARLAcrobotEnv_defaults, ) from carl.envs.classic_control.carl_acrobot import CARLAcrobotEnv from carl.envs.classic_control.carl_cartpole import ( CONTEXT_BOUNDS as CARLCartPoleEnv_bounds, ) from carl.envs.classic_control.carl_cartpole import ( DEFAULT_CONTEXT as CARLCartPoleEnv_defaults, ) from carl.envs.classic_control.carl_cartpole import CARLCartPoleEnv from carl.envs.classic_control.carl_mountaincar import ( CONTEXT_BOUNDS as CARLMountainCarEnv_bounds, ) from carl.envs.classic_control.carl_mountaincar import ( DEFAULT_CONTEXT as CARLMountainCarEnv_defaults, ) from carl.envs.classic_control.carl_mountaincar import CARLMountainCarEnv from carl.envs.classic_control.carl_mountaincarcontinuous import ( CONTEXT_BOUNDS as CARLMountainCarContinuousEnv_bounds, ) from carl.envs.classic_control.carl_mountaincarcontinuous import ( DEFAULT_CONTEXT as CARLMountainCarContinuousEnv_defaults, ) from carl.envs.classic_control.carl_mountaincarcontinuous import ( CARLMountainCarContinuousEnv, ) from carl.envs.classic_control.carl_pendulum import ( CONTEXT_BOUNDS as CARLPendulumEnv_bounds, ) from carl.envs.classic_control.carl_pendulum import ( DEFAULT_CONTEXT as CARLPendulumEnv_defaults, ) from carl.envs.classic_control.carl_pendulum import CARLPendulumEnv
1,525
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CARL-main/carl/envs/classic_control/carl_acrobot.py
from typing import Dict, List, Optional, Union import numpy as np from gym.envs.classic_control import AcrobotEnv from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "link_length_1": 1, # should be seen as 100% default and scaled "link_length_2": 1, # should be seen as 100% default and scaled "link_mass_1": 1, # should be seen as 100% default and scaled "link_mass_2": 1, # should be seen as 100% default and scaled "link_com_1": 0.5, # Percentage of the length of link one "link_com_2": 0.5, # Percentage of the length of link one "link_moi": 1, # should be seen as 100% default and scaled "max_velocity_1": 4 * np.pi, "max_velocity_2": 9 * np.pi, "torque_noise_max": 0.0, # optional noise on torque, sampled uniformly from [-torque_noise_max, torque_noise_max] "initial_angle_lower": -0.1, # lower bound of initial angle distribution (uniform) "initial_angle_upper": 0.1, # upper bound of initial angle distribution (uniform) "initial_velocity_lower": -0.1, # lower bound of initial velocity distribution (uniform) "initial_velocity_upper": 0.1, # upper bound of initial velocity distribution (uniform) } CONTEXT_BOUNDS = { "link_length_1": ( 0.1, 10, float, ), # Links can be shrunken and grown by a factor of 10 "link_length_2": (0.1, 10, float), "link_mass_1": ( 0.1, 10, float, ), # Link mass can be shrunken and grown by a factor of 10 "link_mass_2": (0.1, 10, float), "link_com_1": (0, 1, float), # Center of mass can move from one end to the other "link_com_2": (0, 1, float), "link_moi": ( 0.1, 10, float, ), # Moments on inertia can be shrunken and grown by a factor of 10 "max_velocity_1": ( 0.4 * np.pi, 40 * np.pi, float, ), # Velocity can vary by a factor of 10 in either direction "max_velocity_2": (0.9 * np.pi, 90 * np.pi, float), "torque_noise_max": ( -1.0, 1.0, float, ), # torque is either {-1., 0., 1}. Applying noise of 1. would be quite extreme "initial_angle_lower": (-np.inf, np.inf, float), "initial_angle_upper": (-np.inf, np.inf, float), "initial_velocity_lower": (-np.inf, np.inf, float), "initial_velocity_upper": (-np.inf, np.inf, float), } class CustomAcrobotEnv(AcrobotEnv): INITIAL_ANGLE_LOWER: float = -0.1 INITIAL_ANGLE_UPPER: float = 0.1 INITIAL_VELOCITY_LOWER: float = -0.1 INITIAL_VELOCITY_UPPER: float = 0.1 def reset( self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None, ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: super().reset(seed=seed) low = ( self.INITIAL_ANGLE_LOWER, self.INITIAL_ANGLE_LOWER, self.INITIAL_VELOCITY_LOWER, self.INITIAL_VELOCITY_LOWER, ) high = ( self.INITIAL_ANGLE_UPPER, self.INITIAL_ANGLE_UPPER, self.INITIAL_VELOCITY_UPPER, self.INITIAL_VELOCITY_UPPER, ) self.state = self.np_random.uniform(low=low, high=high).astype(np.float32) if not return_info: return self._get_ob() else: return self._get_ob(), {} class CARLAcrobotEnv(CARLEnv): def __init__( self, env: CustomAcrobotEnv = CustomAcrobotEnv(), contexts: Contexts = {}, hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = 500, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: CustomAcrobotEnv self.env.LINK_LENGTH_1 = self.context["link_length_1"] self.env.LINK_LENGTH_2 = self.context["link_length_2"] self.env.LINK_MASS_1 = self.context["link_mass_1"] self.env.LINK_MASS_2 = self.context["link_mass_2"] self.env.LINK_COM_POS_1 = self.context["link_com_1"] self.env.LINK_COM_POS_2 = self.context["link_com_2"] self.env.LINK_MOI = self.context["link_moi"] self.env.MAX_VEL_1 = self.context["max_velocity_1"] self.env.MAX_VEL_2 = self.context["max_velocity_2"] self.env.torque_noise_max = self.context["torque_noise_max"] self.env.INITIAL_ANGLE_LOWER = self.context["initial_angle_lower"] self.env.INITIAL_ANGLE_UPPER = self.context["initial_angle_upper"] self.env.INITIAL_VELOCITY_LOWER = self.context["initial_velocity_lower"] self.env.INITIAL_VELOCITY_UPPER = self.context["initial_velocity_upper"] high = np.array( [1.0, 1.0, 1.0, 1.0, self.env.MAX_VEL_1, self.env.MAX_VEL_2], dtype=np.float32, ) low = -high self.build_observation_space(low, high, CONTEXT_BOUNDS)
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CARL-main/carl/envs/classic_control/carl_cartpole.py
from typing import Dict, List, Optional, Union import numpy as np from gym.envs.classic_control import CartPoleEnv from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "gravity": 9.8, "masscart": 1.0, # Should be seen as 100% and scaled accordingly "masspole": 0.1, # Should be seen as 100% and scaled accordingly "pole_length": 0.5, # Should be seen as 100% and scaled accordingly "force_magnifier": 10.0, "update_interval": 0.02, # Seconds between updates "initial_state_lower": -0.1, # lower bound of initial state distribution (uniform) (angles and angular velocities) "initial_state_upper": 0.1, # upper bound of initial state distribution (uniform) (angles and angular velocities) } CONTEXT_BOUNDS = { "gravity": (0.1, np.inf, float), # Positive gravity "masscart": (0.1, 10, float), # Cart mass can be varied by a factor of 10 "masspole": (0.01, 1, float), # Pole mass can be varied by a factor of 10 "pole_length": (0.05, 5, float), # Pole length can be varied by a factor of 10 "force_magnifier": (1, 100, int), # Force magnifier can be varied by a factor of 10 "update_interval": ( 0.002, 0.2, float, ), # Update interval can be varied by a factor of 10 "initial_state_lower": (-np.inf, np.inf, float), "initial_state_upper": (-np.inf, np.inf, float), } class CustomCartPoleEnv(CartPoleEnv): def __init__(self) -> None: super().__init__() self.initial_state_lower = -0.05 self.initial_state_upper = 0.05 def reset( self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None, ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: super().reset(seed=seed) self.state = self.np_random.uniform( low=self.initial_state_lower, high=self.initial_state_upper, size=(4,) ) self.steps_beyond_done = None if not return_info: return np.array(self.state, dtype=np.float32) else: return np.array(self.state, dtype=np.float32), {} class CARLCartPoleEnv(CARLEnv): def __init__( self, env: CustomCartPoleEnv = CustomCartPoleEnv(), contexts: Contexts = {}, hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = 500, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: CustomCartPoleEnv self.env.gravity = self.context["gravity"] self.env.masscart = self.context["masscart"] self.env.masspole = self.context["masspole"] self.env.length = self.context["pole_length"] self.env.force_mag = self.context["force_magnifier"] self.env.tau = self.context["update_interval"] self.env.initial_state_lower = self.context["initial_state_lower"] self.env.initial_state_upper = self.context["initial_state_upper"] high = np.array( [ self.env.x_threshold * 2, np.finfo(np.float32).max, self.env.theta_threshold_radians * 2, np.finfo(np.float32).max, ], dtype=np.float32, ) low = -high self.build_observation_space(low, high, CONTEXT_BOUNDS)
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CARL-main/carl/envs/classic_control/carl_mountaincar.py
from typing import Dict, List, Optional, Tuple, Union import gym.envs.classic_control as gccenvs import numpy as np from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "min_position": -1.2, # unit? "max_position": 0.6, # unit? "max_speed": 0.07, # unit? "goal_position": 0.5, # unit? "goal_velocity": 0, # unit? "force": 0.001, # unit? "gravity": 0.0025, # unit? "min_position_start": -0.6, "max_position_start": -0.4, "min_velocity_start": 0.0, "max_velocity_start": 0.0, } CONTEXT_BOUNDS = { "min_position": (-np.inf, np.inf, float), "max_position": (-np.inf, np.inf, float), "max_speed": (0, np.inf, float), "goal_position": (-np.inf, np.inf, float), "goal_velocity": (-np.inf, np.inf, float), "force": (-np.inf, np.inf, float), "gravity": (0, np.inf, float), "min_position_start": (-np.inf, np.inf, float), "max_position_start": (-np.inf, np.inf, float), "min_velocity_start": (-np.inf, np.inf, float), "max_velocity_start": (-np.inf, np.inf, float), } class CustomMountainCarEnv(gccenvs.mountain_car.MountainCarEnv): def __init__(self, goal_velocity: float = 0.0): super(CustomMountainCarEnv, self).__init__(goal_velocity=goal_velocity) self.min_position_start = -0.6 self.max_position_start = -0.4 self.min_velocity_start = 0.0 self.max_velocity_start = 0.0 self.state: np.ndarray # type: ignore [assignment] def sample_initial_state(self) -> np.ndarray: return np.array( [ self.np_random.uniform( low=self.min_position_start, high=self.max_position_start ), self.np_random.uniform( low=self.min_velocity_start, high=self.max_velocity_start ), ] ) def reset( self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None, ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: super().reset(seed=seed) self.state = self.sample_initial_state() if not return_info: return np.array(self.state, dtype=np.float32) else: return np.array(self.state, dtype=np.float32), {} def step(self, action: int) -> Tuple[np.ndarray, float, bool, Dict]: state, reward, done, info = super().step(action) return ( state.squeeze(), reward, done, info, ) # TODO something weird is happening such that the state gets shape (2,1) instead of (2,) class CARLMountainCarEnv(CARLEnv): def __init__( self, env: CustomMountainCarEnv = CustomMountainCarEnv(), contexts: Contexts = {}, hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = 200, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): """ Parameters ---------- env: gym.Env, optional Defaults to classic control environment mountain car from gym (MountainCarEnv). contexts: List[Dict], optional Different contexts / different environment parameter settings. instance_mode: str, optional """ if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: CustomMountainCarEnv self.env.min_position = self.context["min_position"] self.env.max_position = self.context["max_position"] self.env.max_speed = self.context["max_speed"] self.env.goal_position = self.context["goal_position"] self.env.goal_velocity = self.context["goal_velocity"] self.env.min_position_start = self.context["min_position_start"] self.env.max_position_start = self.context["max_position_start"] self.env.min_velocity_start = self.context["min_velocity_start"] self.env.max_velocity_start = self.context["max_velocity_start"] self.env.force = self.context["force"] self.env.gravity = self.context["gravity"] self.low = np.array( [self.env.min_position, -self.env.max_speed], dtype=np.float32 ).squeeze() self.high = np.array( [self.env.max_position, self.env.max_speed], dtype=np.float32 ).squeeze() self.build_observation_space(self.low, self.high, CONTEXT_BOUNDS)
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CARL-main/carl/envs/classic_control/carl_mountaincarcontinuous.py
from typing import Dict, List, Optional, Union import gym.envs.classic_control as gccenvs import numpy as np from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "min_position": -1.2, "max_position": 0.6, "max_speed": 0.07, "goal_position": 0.45, "goal_velocity": 0.0, "power": 0.0015, # "gravity": 0.0025, # currently hardcoded in step "min_position_start": -0.6, "max_position_start": -0.4, "min_velocity_start": 0.0, "max_velocity_start": 0.0, } CONTEXT_BOUNDS = { "min_position": (-np.inf, np.inf, float), "max_position": (-np.inf, np.inf, float), "max_speed": (0, np.inf, float), "goal_position": (-np.inf, np.inf, float), "goal_velocity": (-np.inf, np.inf, float), "power": (-np.inf, np.inf, float), # "force": (-np.inf, np.inf), # "gravity": (0, np.inf), "min_position_start": (-np.inf, np.inf, float), # TODO need to check these "max_position_start": (-np.inf, np.inf, float), "min_velocity_start": (-np.inf, np.inf, float), "max_velocity_start": (-np.inf, np.inf, float), } class CustomMountainCarContinuousEnv( gccenvs.continuous_mountain_car.Continuous_MountainCarEnv ): def __init__(self, goal_velocity: float = 0.0): super(CustomMountainCarContinuousEnv, self).__init__( goal_velocity=goal_velocity ) self.min_position_start = -0.6 self.max_position_start = -0.4 self.min_velocity_start = 0.0 self.max_velocity_start = 0.0 def reset_state(self) -> np.ndarray: return np.array( [ self.np_random.uniform( low=self.min_position_start, high=self.max_position_start ), # sample start position self.np_random.uniform( low=self.min_velocity_start, high=self.max_velocity_start ), # sample start velocity ] ) class CARLMountainCarContinuousEnv(CARLEnv): def __init__( self, env: CustomMountainCarContinuousEnv = CustomMountainCarContinuousEnv(), contexts: Contexts = {}, hide_context: bool = True, add_gaussian_noise_to_context: bool = True, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = 999, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): """ Parameters ---------- env: gym.Env, optional Defaults to classic control environment mountain car from gym (MountainCarEnv). contexts: List[Dict], optional Different contexts / different environment parameter settings. instance_mode: str, optional """ if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: CustomMountainCarContinuousEnv self.env.min_position = self.context["min_position"] self.env.max_position = self.context["max_position"] self.env.max_speed = self.context["max_speed"] self.env.goal_position = self.context["goal_position"] self.env.goal_velocity = self.context["goal_velocity"] self.env.min_position_start = self.context["min_position_start"] self.env.max_position_start = self.context["max_position_start"] self.env.min_velocity_start = self.context["min_velocity_start"] self.env.max_velocity_start = self.context["max_velocity_start"] self.env.power = self.context["power"] # self.env.force = self.context["force"] # self.env.gravity = self.context["gravity"] self.low = np.array( [self.env.min_position, -self.env.max_speed], dtype=np.float32 ).squeeze() self.high = np.array( [self.env.max_position, self.env.max_speed], dtype=np.float32 ).squeeze() self.build_observation_space(self.low, self.high, CONTEXT_BOUNDS)
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CARL-main/carl/envs/classic_control/carl_pendulum.py
from typing import Dict, List, Optional, Union import gym.envs.classic_control as gccenvs import numpy as np from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0, "initial_angle_max": np.pi, # Upper bound for uniform distribution to sample from "initial_velocity_max": 1, # Upper bound for uniform distribution to sample from # The lower bound will be the negative value. } CONTEXT_BOUNDS = { "max_speed": (-np.inf, np.inf, float), "dt": (0, np.inf, float), "g": (0, np.inf, float), "m": (1e-6, np.inf, float), "l": (1e-6, np.inf, float), "initial_angle_max": (0, np.inf, float), "initial_velocity_max": (0, np.inf, float), } class CustomPendulum(gccenvs.pendulum.PendulumEnv): def __init__(self, g: float = 10.0): super(CustomPendulum, self).__init__(g=g) self.initial_angle_max = DEFAULT_CONTEXT["initial_angle_max"] self.initial_velocity_max = DEFAULT_CONTEXT["initial_velocity_max"] def reset( self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None, ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: super().reset(seed=seed) high = np.array([self.initial_angle_max, self.initial_velocity_max]) self.state = self.np_random.uniform(low=-high, high=high) self.last_u = None if not return_info: return self._get_obs() else: return self._get_obs(), {} class CARLPendulumEnv(CARLEnv): def __init__( self, env: CustomPendulum = CustomPendulum(), contexts: Contexts = {}, hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = 200, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): """ Max torque is not a context feature because it changes the action space. Parameters ---------- env contexts instance_mode hide_context add_gaussian_noise_to_context gaussian_noise_std_percentage """ if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list( DEFAULT_CONTEXT.keys() ) # allow to augment all values def _update_context(self) -> None: self.env: CustomPendulum self.env.max_speed = self.context["max_speed"] self.env.dt = self.context["dt"] self.env.l = self.context["l"] # noqa: E741 ambiguous variable name self.env.m = self.context["m"] self.env.g = self.context["g"] self.env.initial_angle_max = self.context["initial_angle_max"] self.env.initial_velocity_max = self.context["initial_velocity_max"] high = np.array([1.0, 1.0, self.max_speed], dtype=np.float32) self.build_observation_space(-high, high, CONTEXT_BOUNDS)
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CARL-main/carl/envs/dmc/__init__.py
# flake8: noqa: F401 # Contexts and bounds by name from carl.envs.dmc.carl_dm_finger import CONTEXT_BOUNDS as CARLDmcFingerEnv_bounds from carl.envs.dmc.carl_dm_finger import CONTEXT_MASK as CARLDmcFingerEnv_mask from carl.envs.dmc.carl_dm_finger import DEFAULT_CONTEXT as CARLDmcFingerEnv_defaults from carl.envs.dmc.carl_dm_finger import CARLDmcFingerEnv from carl.envs.dmc.carl_dm_fish import CONTEXT_BOUNDS as CARLDmcFishEnv_bounds from carl.envs.dmc.carl_dm_fish import CONTEXT_MASK as CARLDmcFishEnv_mask from carl.envs.dmc.carl_dm_fish import DEFAULT_CONTEXT as CARLDmcFishEnv_defaults from carl.envs.dmc.carl_dm_fish import CARLDmcFishEnv from carl.envs.dmc.carl_dm_quadruped import CONTEXT_BOUNDS as CARLDmcQuadrupedEnv_bounds from carl.envs.dmc.carl_dm_quadruped import CONTEXT_MASK as CARLDmcQuadrupedEnv_mask from carl.envs.dmc.carl_dm_quadruped import ( DEFAULT_CONTEXT as CARLDmcQuadrupedEnv_defaults, ) from carl.envs.dmc.carl_dm_quadruped import CARLDmcQuadrupedEnv from carl.envs.dmc.carl_dm_walker import CONTEXT_MASK as CARLDmcWalkerEnv_mask from carl.envs.dmc.carl_dm_walker import DEFAULT_CONTEXT as CARLDmcWalkerEnv_defaults from carl.envs.dmc.carl_dm_walker import CARLDmcWalkerEnv
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CARL-main/carl/envs/dmc/carl_dm_finger.py
from typing import Dict, List, Optional, Union import numpy as np from carl.context.selection import AbstractSelector from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv from carl.envs.dmc.dmc_tasks.fish import STEP_LIMIT # type: ignore from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "gravity": -9.81, # Gravity is disabled via flag "friction_tangential": 1, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1, # Scaling factor for rolling friction of all geoms (objects) "timestep": 0.004, # Seconds between updates "joint_damping": 1.0, # Scaling factor for all joints "joint_stiffness": 0.0, "actuator_strength": 1, # Scaling factor for all actuators in the model "density": 5000.0, "viscosity": 0.0, "geom_density": 1.0, # No effect, because no gravity "wind_x": 0.0, "wind_y": 0.0, "wind_z": 0.0, "limb_length_0": 0.17, "limb_length_1": 0.16, "spinner_radius": 0.04, "spinner_length": 0.18, } CONTEXT_BOUNDS = { "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), "timestep": ( 0.001, 0.1, float, ), "joint_damping": (0, np.inf, float), "joint_stiffness": (0, np.inf, float), "actuator_strength": (0, np.inf, float), "density": (0, np.inf, float), "viscosity": (0, np.inf, float), "geom_density": (0, np.inf, float), "wind_x": (-np.inf, np.inf, float), "wind_y": (-np.inf, np.inf, float), "wind_z": (-np.inf, np.inf, float), "limb_length_0": (0.01, 0.2, float), "limb_length_1": (0.01, 0.2, float), "spinner_radius": (0.01, 0.05, float), "spinner_length": (0.01, 0.4, float), } CONTEXT_MASK = [ "gravity", "geom_density", "wind_x", "wind_y", "wind_z", ] class CARLDmcFingerEnv(CARLDmcEnv): def __init__( self, domain: str = "finger", task: str = "spin_context", contexts: Contexts = {}, context_mask: Optional[List[str]] = [], hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = STEP_LIMIT, state_context_features: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): super().__init__( domain=domain, task=task, contexts=contexts, context_mask=context_mask, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, )
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CARL-main/carl/envs/dmc/carl_dm_fish.py
from typing import Dict, List, Optional, Union import numpy as np from carl.context.selection import AbstractSelector from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv from carl.envs.dmc.dmc_tasks.fish import STEP_LIMIT # type: ignore from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "gravity": -9.81, # Gravity is disabled via flag "friction_tangential": 1, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1, # Scaling factor for rolling friction of all geoms (objects) "timestep": 0.004, # Seconds between updates "joint_damping": 1.0, # Scaling factor for all joints "joint_stiffness": 0.0, "actuator_strength": 1, # Scaling factor for all actuators in the model "density": 5000.0, "viscosity": 0.0, "geom_density": 1.0, # No effect, because no gravity "wind_x": 0.0, "wind_y": 0.0, "wind_z": 0.0, } CONTEXT_BOUNDS = { "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), "timestep": ( 0.001, 0.1, float, ), "joint_damping": (0, np.inf, float), "joint_stiffness": (0, np.inf, float), "actuator_strength": (0, np.inf, float), "density": (0, np.inf, float), "viscosity": (0, np.inf, float), "geom_density": (0, np.inf, float), "wind_x": (-np.inf, np.inf, float), "wind_y": (-np.inf, np.inf, float), "wind_z": (-np.inf, np.inf, float), } CONTEXT_MASK = [ "gravity", "geom_density", "wind_x", "wind_y", "wind_z", ] class CARLDmcFishEnv(CARLDmcEnv): def __init__( self, domain: str = "fish", task: str = "swim_context", contexts: Contexts = {}, context_mask: Optional[List[str]] = [], hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = STEP_LIMIT, state_context_features: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): super().__init__( domain=domain, task=task, contexts=contexts, context_mask=context_mask, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, )
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CARL-main/carl/envs/dmc/carl_dm_quadruped.py
from typing import Dict, List, Optional, Union import numpy as np from carl.context.selection import AbstractSelector from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv from carl.envs.dmc.dmc_tasks.quadruped import STEP_LIMIT # type: ignore from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "gravity": -9.81, "friction_tangential": 1.0, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1.0, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1.0, # Scaling factor for rolling friction of all geoms (objects) "timestep": 0.005, # Seconds between updates "joint_damping": 1.0, # Scaling factor for all joints "joint_stiffness": 0.0, "actuator_strength": 1, # Scaling factor for all actuators in the model "density": 0.0, "viscosity": 0.0, "geom_density": 1.0, # Scaling factor for all geom (objects) densities "wind_x": 0.0, "wind_y": 0.0, "wind_z": 0.0, } CONTEXT_BOUNDS = { "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), "timestep": ( 0.001, 0.1, float, ), "joint_damping": (0, np.inf, float), "joint_stiffness": (0, np.inf, float), "actuator_strength": (0, np.inf, float), "density": (0, np.inf, float), "viscosity": (0, np.inf, float), "geom_density": (0, np.inf, float), "wind_x": (-np.inf, np.inf, float), "wind_y": (-np.inf, np.inf, float), "wind_z": (-np.inf, np.inf, float), } CONTEXT_MASK = [ "wind_x", "wind_y", "wind_z", ] class CARLDmcQuadrupedEnv(CARLDmcEnv): def __init__( self, domain: str = "quadruped", task: str = "walk_context", contexts: Contexts = {}, context_mask: Optional[List[str]] = [], hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = STEP_LIMIT, state_context_features: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): super().__init__( domain=domain, task=task, contexts=contexts, context_mask=context_mask, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, )
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CARL-main/carl/envs/dmc/carl_dm_walker.py
from typing import Dict, List, Optional, Union import numpy as np from carl.context.selection import AbstractSelector from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv from carl.envs.dmc.dmc_tasks.walker import STEP_LIMIT # type: ignore from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { "gravity": -9.81, "friction_tangential": 1.0, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1.0, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1.0, # Scaling factor for rolling friction of all geoms (objects) "timestep": 0.0025, # Seconds between updates "joint_damping": 1.0, # Scaling factor for all joints "joint_stiffness": 0.0, "actuator_strength": 1.0, # Scaling factor for all actuators in the model "density": 0.0, "viscosity": 0.0, "geom_density": 1.0, # Scaling factor for all geom (objects) densities "wind_x": 0.0, "wind_y": 0.0, "wind_z": 0.0, } CONTEXT_BOUNDS = { "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), "timestep": ( 0.001, 0.1, float, ), "joint_damping": (0, np.inf, float), "joint_stiffness": (0, np.inf, float), "actuator_strength": (0, np.inf, float), "density": (0, np.inf, float), "viscosity": (0, np.inf, float), "geom_density": (0, np.inf, float), "wind_x": (-np.inf, np.inf, float), "wind_y": (-np.inf, np.inf, float), "wind_z": (-np.inf, np.inf, float), } CONTEXT_MASK = [ "wind_x", "wind_y", "wind_z", ] class CARLDmcWalkerEnv(CARLDmcEnv): def __init__( self, domain: str = "walker", task: str = "walk_context", contexts: Contexts = {}, context_mask: Optional[List[str]] = [], hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = STEP_LIMIT, state_context_features: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): super().__init__( domain=domain, task=task, contexts=contexts, context_mask=context_mask, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, )
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CARL-main/carl/envs/dmc/carl_dmcontrol.py
from typing import Dict, List, Optional, Union from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.envs.dmc.loader import load_dmc_env from carl.envs.dmc.wrappers import MujocoToGymWrapper from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts class CARLDmcEnv(CARLEnv): """ General class for the dm-control environments. Meta-class to change the context for the environments. Parameters ---------- domain : str Dm-control domain that should be loaded. task : str Task within the specified domain. For descriptions of the other parameters see the parent class CARLEnv. Raises ------ NotImplementedError Dict observation spaces are not implemented for dm-control yet. """ def __init__( self, domain: str, task: str, contexts: Contexts, context_mask: Optional[List[str]], hide_context: bool, add_gaussian_noise_to_context: bool, gaussian_noise_std_percentage: float, logger: Optional[TrialLogger], scale_context_features: str, default_context: Optional[Context], max_episode_length: int, state_context_features: Optional[List[str]], dict_observation_space: bool, context_selector: Optional[Union[AbstractSelector, type[AbstractSelector]]], context_selector_kwargs: Optional[Dict], ): # TODO can we have more than 1 env? if not contexts: contexts = {0: default_context} # type: ignore self.domain = domain self.task = task env = load_dmc_env( domain_name=self.domain, task_name=self.task, context={}, context_mask=[], environment_kwargs={"flat_observation": True}, ) env = MujocoToGymWrapper(env) super().__init__( env=env, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) # TODO check gaussian noise on context features self.whitelist_gaussian_noise = list( default_context.keys() # type: ignore ) # allow to augment all values def _update_context(self) -> None: env = load_dmc_env( domain_name=self.domain, task_name=self.task, context=self.context, context_mask=self.context_mask, environment_kwargs={"flat_observation": True}, ) self.env = MujocoToGymWrapper(env)
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CARL-main/carl/envs/dmc/loader.py
from typing import Any, Dict, List, Optional import inspect import dm_env # type: ignore from dm_control import suite # type: ignore from carl.envs.dmc.dmc_tasks import ( # type: ignore [import] # noqa: F401 finger, fish, quadruped, walker, ) from carl.utils.types import Context _DOMAINS = { name: module for name, module in locals().items() if inspect.ismodule(module) and hasattr(module, "SUITE") } def load_dmc_env( domain_name: str, task_name: str, context: Context = {}, context_mask: Optional[List[str]] = [], task_kwargs: Optional[Any] = None, environment_kwargs: Dict[str, bool] = None, visualize_reward: bool = False, ) -> dm_env: if domain_name in _DOMAINS: domain = _DOMAINS[domain_name] elif domain_name in suite._DOMAINS: domain = suite._DOMAINS[domain_name] else: raise ValueError("Domain {!r} does not exist.".format(domain_name)) if task_name in domain.SUITE: task_kwargs = task_kwargs or {} if environment_kwargs is not None: task_kwargs = dict(task_kwargs, environment_kwargs=environment_kwargs) env = domain.SUITE[task_name]( context=context, context_mask=context_mask, **task_kwargs ) env.task.visualize_reward = visualize_reward return env elif (domain_name, task_name) in suite.ALL_TASKS: return suite.load( domain_name=domain_name, task_name=task_name, task_kwargs=task_kwargs, environment_kwargs=environment_kwargs, visualize_reward=visualize_reward, ) else: raise ValueError( "Task {!r} does not exist in domain {!r}.".format(task_name, domain_name) )
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CARL-main/carl/envs/dmc/wrappers.py
from typing import Any, Optional, Tuple, TypeVar, Union import dm_env # type: ignore import gym import numpy as np from dm_env import StepType from gym import spaces ObsType = TypeVar("ObsType") ActType = TypeVar("ActType") def get_shape(shape: tuple) -> tuple: """ Get shape of array or scalar. If scalar (shape = ()), return (1,). Parameters ---------- shape: tuple Shape of array, can be empty tuple Returns ------- Shape: Same as before if not empty, else (1,) """ return shape if shape else (1,) class MujocoToGymWrapper(gym.Env): def __init__(self, env: dm_env) -> None: # TODO set seeds self.env = env action_spec = self.env.action_spec() self.action_space = spaces.Box( action_spec.minimum, action_spec.maximum, dtype=action_spec.dtype ) obs_spec = self.env.observation_spec() # obs_spaces = { # k: spaces.Box(low=-np.inf, high=np.inf, shape=v.shape, dtype=v.dtype) # for k, v in obs_spec.items() # } # self.observation_space = spaces.Dict(spaces=obs_spaces) # TODO add support for Dict Spaces in CARLEnv (later) shapes = [int(np.sum([get_shape(v.shape) for v in obs_spec.values()]))] lows = np.array([-np.inf] * shapes[0]) highs = np.array([np.inf] * shapes[0]) dtype = np.unique([[v.dtype for v in obs_spec.values()]])[0] self.observation_space = spaces.Box( low=lows, high=highs, shape=shapes, dtype=dtype ) def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]: """Run one timestep of the environment's dynamics. When end of episode is reached, you are responsible for calling `reset()` to reset this environment's state. Accepts an action and returns a tuple (observation, reward, done, info). Args: action (object): an action provided by the agent Returns: observation (object): agent's observation of the current environment reward (float) : amount of reward returned after previous action done (bool): whether the episode has ended, in which case further step() calls will return undefined results info (dict): contains auxiliary diagnostic information (helpful for debugging, logging, and sometimes learning) """ timestep = self.env.step(action=action) step_type: StepType = timestep.step_type reward = timestep.reward discount = timestep.discount observation = timestep.observation["observations"] info = {"step_type": step_type, "discount": discount} done = step_type == StepType.LAST return observation, reward, done, info def reset( self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None, ) -> Union[ObsType, tuple[ObsType, dict]]: super(MujocoToGymWrapper, self).reset( seed=seed, return_info=return_info, options=options ) timestep = self.env.reset() if isinstance(self.observation_space, spaces.Box): observation = timestep.observation["observations"] else: raise NotImplementedError return observation def render( self, mode: str = "human", camera_id: int = 0, **kwargs: Any ) -> np.ndarray: """Renders the environment. The set of supported modes varies per environment. (And some third-party environments may not support rendering at all.) By convention, if mode is: - human: render to the current display or terminal and return nothing. Usually for human consumption. - rgb_array: Return an numpy.ndarray with shape (x, y, 3), representing RGB values for an x-by-y pixel image, suitable for turning into a video. - ansi: Return a string (str) or StringIO.StringIO containing a terminal-style text representation. The text can include newlines and ANSI escape sequences (e.g. for colors). Note: Make sure that your class's metadata 'render_modes' key includes the list of supported modes. It's recommended to call super() in implementations to use the functionality of this method. Args: mode (str): the mode to render with camera_id kwargs: Keyword arguments for dm_control.mujoco.engine.Physics.render Example: class MyEnv(Env): metadata = {'render_modes': ['human', 'rgb_array']} def render(self, mode='human'): if mode == 'rgb_array': return np.array(...) # return RGB frame suitable for video elif mode == 'human': ... # pop up a window and render else: super(MyEnv, self).render(mode=mode) # just raise an exception """ # TODO render mujoco human version if mode == "human": raise NotImplementedError elif mode == "rgb_array": return self.env.physics.render(camera_id=camera_id, **kwargs) else: raise NotImplementedError
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CARL-main/carl/envs/dmc/dmc_tasks/finger.py
# flake8: noqa: E501 # Copyright 2017 The dm_control Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Finger Domain.""" from __future__ import annotations from typing import Any from multiprocessing.sharedctypes import Value import numpy as np from dm_control.rl import control # type: ignore from dm_control.suite.finger import ( # type: ignore _CONTROL_TIMESTEP, _DEFAULT_TIME_LIMIT, _EASY_TARGET_SIZE, _HARD_TARGET_SIZE, SUITE, Physics, Spin, Turn, get_model_and_assets, ) from carl.envs.dmc.dmc_tasks.utils import adapt_context # type: ignore from carl.utils.types import Context def check_constraints( spinner_length: float, limb_length_0: float, limb_length_1: float, x_spinner: float = 0.2, x_finger: float = -0.2, ) -> None: spinner_half_length = spinner_length / 2 # Check if spinner collides with finger hinge distance_spinner_to_fingerhinge = (x_spinner - x_finger) - spinner_half_length if distance_spinner_to_fingerhinge < 0: raise ValueError( f"Distance finger to spinner ({distance_spinner_to_fingerhinge}) not big enough, " f"spinner can't spin. Decrease spinner_length ({spinner_length})." ) # Check if finger can reach spinner (distance should be negative) distance_fingertip_to_spinner = (x_spinner - spinner_half_length) - ( x_finger + limb_length_0 + limb_length_1 ) if distance_fingertip_to_spinner > 0: raise ValueError( f"Finger cannot reach spinner ({distance_fingertip_to_spinner}). Increase either " f"limb_length_0, limb_length_1 or spinner_length." ) def get_finger_xml_string( limb_length_0: float = 0.17, limb_length_1: float = 0.16, spinner_radius: float = 0.04, spinner_length: float = 0.18, **kwargs: Any, ) -> bytes: # Finger position x_finger = -0.2 y_finger = 0.4 # Spinner position x_spinner = 0.2 y_spinner = 0.4 # Target position y_target = 0.4 # Spinner geometry spinner_half_length = spinner_length / 2 spinner_tip_radius = 0.02 distance_spinner_tip_to_captop = 0.06 y_spinner_tip = ( spinner_half_length + distance_spinner_tip_to_captop - spinner_tip_radius ) # originally 0.13 check_constraints( limb_length_0=limb_length_0, limb_length_1=limb_length_1, x_spinner=x_spinner, x_finger=x_finger, spinner_length=spinner_length, ) proximal_to = -limb_length_0 xml_string = f""" <mujoco model="finger"> <include file="./common/visual.xml"/> <include file="./common/skybox.xml"/> <include file="./common/materials.xml"/> <option timestep="0.01" cone="elliptic" iterations="200"> <flag gravity="disable"/> </option> <default> <geom solimp="0 0.9 0.01" solref=".02 1"/> <joint type="hinge" axis="0 -1 0"/> <motor ctrllimited="true" ctrlrange="-1 1"/> <default class="finger"> <joint damping="2.5" limited="true"/> <site type="ellipsoid" size=".025 .03 .025" material="site" group="3"/> </default> </default> <worldbody> <light name="light" directional="true" diffuse=".6 .6 .6" pos="0 0 2" specular=".3 .3 .3"/> <geom name="ground" type="plane" pos="0 0 0" size=".6 .2 10" material="grid"/> <camera name="cam0" pos="0 -1 .8" xyaxes="1 0 0 0 1 2"/> <camera name="cam1" pos="0 -1 .4" xyaxes="1 0 0 0 0 1" /> <body name="proximal" pos="{x_finger} 0 {y_finger}" childclass="finger"> <geom name="proximal_decoration" type="cylinder" fromto="0 -.033 0 0 .033 0" size=".034" material="decoration"/> <joint name="proximal" range="-110 110" ref="-90"/> <geom name="proximal" type="capsule" material="self" size=".03" fromto="0 0 0 0 0 {proximal_to}"/> <body name="distal" pos="0 0 {proximal_to - 0.01}" childclass="finger"> <joint name="distal" range="-110 110"/> <geom name="distal" type="capsule" size=".028" material="self" fromto="0 0 0 0 0 {-limb_length_1}" contype="0" conaffinity="0"/> <geom name="fingertip" type="capsule" size=".03" material="effector" fromto="0 0 {-limb_length_1 - 0.03} 0 0 {-limb_length_1 - 0.001}"/> <site name="touchtop" pos=".01 0 -.17"/> <site name="touchbottom" pos="-.01 0 -.17"/> </body> </body> <body name="spinner" pos="{x_spinner} 0 {y_spinner}"> <joint name="hinge" frictionloss=".1" damping=".5"/> <geom name="cap1" type="capsule" size="{spinner_radius}" fromto="{spinner_radius/2} 0 {-spinner_half_length} {spinner_radius} 0 {spinner_half_length}" material="self"/> <geom name="cap2" type="capsule" size="{spinner_radius}" fromto="{-spinner_radius/2} 0 {-spinner_half_length} 0 0 {spinner_half_length}" material="self"/> <site name="tip" type="sphere" size="{spinner_tip_radius}" pos="0 0 {y_spinner_tip}" material="target"/> <geom name="spinner_decoration" type="cylinder" fromto="0 -.045 0 0 .045 0" size="{spinner_radius/2}" material="decoration"/> </body> <site name="target" type="sphere" size=".03" pos="0 0 {y_target}" material="target"/> </worldbody> <actuator> <motor name="proximal" joint="proximal" gear="30"/> <motor name="distal" joint="distal" gear="15"/> </actuator> <!-- All finger observations are functions of sensors. This is useful for finite-differencing. --> <sensor> <jointpos name="proximal" joint="proximal"/> <jointpos name="distal" joint="distal"/> <jointvel name="proximal_velocity" joint="proximal"/> <jointvel name="distal_velocity" joint="distal"/> <jointvel name="hinge_velocity" joint="hinge"/> <framepos name="tip" objtype="site" objname="tip"/> <framepos name="target" objtype="site" objname="target"/> <framepos name="spinner" objtype="xbody" objname="spinner"/> <touch name="touchtop" site="touchtop"/> <touch name="touchbottom" site="touchbottom"/> <framepos name="touchtop_pos" objtype="site" objname="touchtop"/> <framepos name="touchbottom_pos" objtype="site" objname="touchbottom"/> </sensor> </mujoco> """ xml_string_bytes = xml_string.encode() return xml_string_bytes @SUITE.add("benchmarking") # type: ignore[misc] def spin_context( context: Context = {}, context_mask: list = [], time_limit: float = _DEFAULT_TIME_LIMIT, random: np.random.RandomState | int | None = None, environment_kwargs: dict | None = None, ) -> control.Environment: """Returns the Spin task.""" xml_string, assets = get_model_and_assets() xml_string = get_finger_xml_string(**context) if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = Spin(random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) @SUITE.add("benchmarking") # type: ignore[misc] def turn_easy_context( context: Context = {}, context_mask: list = [], time_limit: float = _DEFAULT_TIME_LIMIT, random: np.random.RandomState | int | None = None, environment_kwargs: dict | None = None, ) -> control.Environment: """Returns the easy Turn task.""" xml_string, assets = get_model_and_assets() xml_string = get_finger_xml_string(**context) if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = Turn(target_radius=_EASY_TARGET_SIZE, random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) @SUITE.add("benchmarking") # type: ignore[misc] def turn_hard_context( context: Context = {}, context_mask: list = [], time_limit: float = _DEFAULT_TIME_LIMIT, random: np.random.RandomState | int | None = None, environment_kwargs: dict | None = None, ) -> control.Environment: """Returns the hard Turn task.""" xml_string, assets = get_model_and_assets() xml_string = get_finger_xml_string(**context) if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = Turn(target_radius=_HARD_TARGET_SIZE, random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, )
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CARL-main/carl/envs/dmc/dmc_tasks/fish.py
# Copyright 2017 The dm_control Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Fish Domain.""" from typing import Dict, List, Optional, Tuple, Union import collections import dm_env # type: ignore import numpy as np from dm_control import mujoco # type: ignore from dm_control.rl import control # type: ignore from dm_control.suite import base, common # type: ignore from dm_control.utils import containers, rewards # type: ignore from carl.envs.dmc.dmc_tasks.utils import adapt_context # type: ignore from carl.utils.types import Context _DEFAULT_TIME_LIMIT = 40 _CONTROL_TIMESTEP = 0.04 STEP_LIMIT = 1000 _JOINTS = [ "tail1", "tail_twist", "tail2", "finright_roll", "finright_pitch", "finleft_roll", "finleft_pitch", ] SUITE = containers.TaggedTasks() def get_model_and_assets() -> Tuple[bytes, Dict]: """Returns a tuple containing the model XML string and a dict of assets.""" return common.read_model("fish.xml"), common.ASSETS @SUITE.add("benchmarking") # type: ignore def upright_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Fish Upright task.""" xml_string, assets = get_model_and_assets() if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = Upright(random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, control_timestep=_CONTROL_TIMESTEP, time_limit=time_limit, **environment_kwargs, ) @SUITE.add("benchmarking") # type: ignore def swim_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Fish Swim task.""" xml_string, assets = get_model_and_assets() if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = Swim(random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, control_timestep=_CONTROL_TIMESTEP, time_limit=time_limit, **environment_kwargs, ) class Physics(mujoco.Physics): """Physics simulation with additional features for the Fish domain.""" def upright(self) -> np.float64: """Returns projection from z-axes of torso to the z-axes of worldbody.""" return self.named.data.xmat["torso", "zz"] def torso_velocity(self) -> np.ndarray: """Returns velocities and angular velocities of the torso.""" return self.data.sensordata def joint_velocities(self) -> np.ndarray: """Returns the joint velocities.""" return self.named.data.qvel[_JOINTS] def joint_angles(self) -> np.ndarray: """Returns the joint positions.""" return self.named.data.qpos[_JOINTS] def mouth_to_target(self) -> np.ndarray: """Returns a vector, from mouth to target in local coordinate of mouth.""" data = self.named.data mouth_to_target_global = data.geom_xpos["target"] - data.geom_xpos["mouth"] return mouth_to_target_global.dot(data.geom_xmat["mouth"].reshape(3, 3)) class Upright(base.Task): """A Fish `Task` for getting the torso upright with smooth reward.""" def __init__(self, random: Union[np.random.RandomState, int, None] = None) -> None: """Initializes an instance of `Upright`. Args: random: Either an existing `numpy.random.RandomState` instance, an integer seed for creating a new `RandomState`, or None to select a seed automatically. """ super().__init__(random=random) def initialize_episode(self, physics: Physics) -> None: """Randomizes the tail and fin angles and the orientation of the Fish.""" quat = self.random.randn(4) physics.named.data.qpos["root"][3:7] = quat / np.linalg.norm(quat) for joint in _JOINTS: physics.named.data.qpos[joint] = self.random.uniform(-0.2, 0.2) # Hide the target. It's irrelevant for this task. physics.named.model.geom_rgba["target", 3] = 0 super().initialize_episode(physics) def get_observation(self, physics: Physics) -> collections.OrderedDict: """Returns an observation of joint angles, velocities and uprightness.""" obs = collections.OrderedDict() obs["joint_angles"] = physics.joint_angles() obs["upright"] = physics.upright() # type: ignore obs["velocity"] = physics.velocity() return obs def get_reward(self, physics: Physics) -> float: """Returns a smooth reward.""" return rewards.tolerance(physics.upright(), bounds=(1, 1), margin=1) class Swim(base.Task): """A Fish `Task` for swimming with smooth reward.""" def __init__(self, random: Union[np.random.RandomState, int, None] = None) -> None: """Initializes an instance of `Swim`. Args: random: Optional, either a `numpy.random.RandomState` instance, an integer seed for creating a new `RandomState`, or None to select a seed automatically (default). """ super().__init__(random=random) def initialize_episode(self, physics: Physics) -> None: """Sets the state of the environment at the start of each episode.""" quat = self.random.randn(4) physics.named.data.qpos["root"][3:7] = quat / np.linalg.norm(quat) for joint in _JOINTS: physics.named.data.qpos[joint] = self.random.uniform(-0.2, 0.2) # Randomize target position. physics.named.model.geom_pos["target", "x"] = self.random.uniform(-0.4, 0.4) physics.named.model.geom_pos["target", "y"] = self.random.uniform(-0.4, 0.4) physics.named.model.geom_pos["target", "z"] = self.random.uniform(0.1, 0.3) super().initialize_episode(physics) def get_observation(self, physics: Physics) -> collections.OrderedDict: """Returns an observation of joints, target direction and velocities.""" obs = collections.OrderedDict() obs["joint_angles"] = physics.joint_angles() obs["upright"] = physics.upright() # type: ignore obs["target"] = physics.mouth_to_target() obs["velocity"] = physics.velocity() return obs def get_reward(self, physics: Physics) -> np.float64: """Returns a smooth reward.""" radii = physics.named.model.geom_size[["mouth", "target"], 0].sum() in_target = rewards.tolerance( np.linalg.norm(physics.mouth_to_target()), bounds=(0, radii), margin=2 * radii, ) is_upright = 0.5 * (physics.upright() + 1) return (7 * in_target + is_upright) / 8
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CARL-main/carl/envs/dmc/dmc_tasks/quadruped.py
# Copyright 2019 The dm_control Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Quadruped Domain.""" from typing import Any, Dict, List, Optional, Union import collections from collections import OrderedDict import dm_env # type: ignore import numpy as np from dm_control import mujoco # type: ignore from dm_control.mujoco.wrapper import mjbindings # type: ignore from dm_control.mujoco.wrapper.core import MjData # type: ignore from dm_control.rl import control # type: ignore from dm_control.suite import base, common # type: ignore from dm_control.utils import containers, rewards, xml_tools # type: ignore from lxml import etree # type: ignore from scipy import ndimage from carl.envs.dmc.dmc_tasks.utils import adapt_context # type: ignore from carl.utils.types import Context enums = mjbindings.enums mjlib = mjbindings.mjlib _DEFAULT_TIME_LIMIT = 20 _CONTROL_TIMESTEP = 0.02 STEP_LIMIT = 1000 # Horizontal speeds above which the move reward is 1. _RUN_SPEED = 5 _WALK_SPEED = 0.5 # Constants related to terrain generation. _HEIGHTFIELD_ID = 0 _TERRAIN_SMOOTHNESS = 0.15 # 0.0: maximally bumpy; 1.0: completely smooth. _TERRAIN_BUMP_SCALE = 2 # Spatial scale of terrain bumps (in meters). # Named model elements. _TOES = ["toe_front_left", "toe_back_left", "toe_back_right", "toe_front_right"] _WALLS = ["wall_px", "wall_py", "wall_nx", "wall_ny"] SUITE = containers.TaggedTasks() def make_model( floor_size: Optional[Any] = None, terrain: bool = False, rangefinders: bool = False, walls_and_ball: bool = False, ) -> bytes: """Returns the model XML string.""" xml_string = common.read_model("quadruped.xml") parser = etree.XMLParser(remove_blank_text=True) mjcf = etree.XML(xml_string, parser) # Set floor size. if floor_size is not None: floor_geom = mjcf.find(".//geom[@name='floor']") floor_geom.attrib["size"] = f"{floor_size} {floor_size} .5" # Remove walls, ball and target. if not walls_and_ball: for wall in _WALLS: wall_geom = xml_tools.find_element(mjcf, "geom", wall) wall_geom.getparent().remove(wall_geom) # Remove ball. ball_body = xml_tools.find_element(mjcf, "body", "ball") ball_body.getparent().remove(ball_body) # Remove target. target_site = xml_tools.find_element(mjcf, "site", "target") target_site.getparent().remove(target_site) # Remove terrain. if not terrain: terrain_geom = xml_tools.find_element(mjcf, "geom", "terrain") terrain_geom.getparent().remove(terrain_geom) # Remove rangefinders if they're not used, as range computations can be # expensive, especially in a scene with heightfields. if not rangefinders: rangefinder_sensors = mjcf.findall(".//rangefinder") for rf in rangefinder_sensors: rf.getparent().remove(rf) return etree.tostring(mjcf, pretty_print=True) @SUITE.add() # type: ignore def walk_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Walk task with the adapted context.""" xml_string = make_model(floor_size=_DEFAULT_TIME_LIMIT * _WALK_SPEED) if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Move(desired_speed=_WALK_SPEED, random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) @SUITE.add() # type: ignore def run_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Run task with the adapted context.""" xml_string = make_model(floor_size=_DEFAULT_TIME_LIMIT * _RUN_SPEED) if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Move(desired_speed=_RUN_SPEED, random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) @SUITE.add() # type: ignore def escape_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Escape task with the adapted context.""" xml_string = make_model(floor_size=40, terrain=True, rangefinders=True) if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Escape(random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) @SUITE.add() # type: ignore def fetch_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Fetch task with the adapted context.""" xml_string = make_model(walls_and_ball=True) if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Fetch(random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) class Physics(mujoco.Physics): """Physics simulation with additional features for the Quadruped domain.""" def _reload_from_data(self, data: MjData) -> None: super()._reload_from_data(data) # Clear cached sensor names when the physics is reloaded. self._sensor_types_to_names: Dict = {} self._hinge_names: List = [] def _get_sensor_names(self, *sensor_types: List[int]) -> List[str]: try: sensor_names = self._sensor_types_to_names[sensor_types] except KeyError: [sensor_ids] = np.where(np.in1d(self.model.sensor_type, sensor_types)) sensor_names = [self.model.id2name(s_id, "sensor") for s_id in sensor_ids] self._sensor_types_to_names[sensor_types] = sensor_names return sensor_names def torso_upright(self) -> np.ndarray: """Returns the dot-product of the torso z-axis and the global z-axis.""" return np.asarray(self.named.data.xmat["torso", "zz"]) def torso_velocity(self) -> np.ndarray: """Returns the velocity of the torso, in the local frame.""" return self.named.data.sensordata["velocimeter"].copy() def egocentric_state(self) -> np.ndarray: """Returns the state without global orientation or position.""" if not self._hinge_names: [hinge_ids] = np.nonzero(self.model.jnt_type == enums.mjtJoint.mjJNT_HINGE) self._hinge_names = [ self.model.id2name(j_id, "joint") for j_id in hinge_ids ] return np.hstack( ( self.named.data.qpos[self._hinge_names], self.named.data.qvel[self._hinge_names], self.data.act, ) ) def toe_positions(self) -> np.ndarray: """Returns toe positions in egocentric frame.""" torso_frame = self.named.data.xmat["torso"].reshape(3, 3) torso_pos = self.named.data.xpos["torso"] torso_to_toe = self.named.data.xpos[_TOES] - torso_pos return torso_to_toe.dot(torso_frame) def force_torque(self) -> np.ndarray: """Returns scaled force/torque sensor readings at the toes.""" force_torque_sensors = self._get_sensor_names( enums.mjtSensor.mjSENS_FORCE, enums.mjtSensor.mjSENS_TORQUE ) return np.arcsinh(self.named.data.sensordata[force_torque_sensors]) def imu(self) -> np.ndarray: """Returns IMU-like sensor readings.""" imu_sensors = self._get_sensor_names( enums.mjtSensor.mjSENS_GYRO, enums.mjtSensor.mjSENS_ACCELEROMETER ) return self.named.data.sensordata[imu_sensors] def rangefinder(self) -> np.ndarray: """Returns scaled rangefinder sensor readings.""" rf_sensors = self._get_sensor_names(enums.mjtSensor.mjSENS_RANGEFINDER) rf_readings = self.named.data.sensordata[rf_sensors] no_intersection = -1.0 return np.where(rf_readings == no_intersection, 1.0, np.tanh(rf_readings)) def origin_distance(self) -> np.ndarray: """Returns the distance from the origin to the workspace.""" return np.asarray(np.linalg.norm(self.named.data.site_xpos["workspace"])) def origin(self) -> np.ndarray: """Returns origin position in the torso frame.""" torso_frame = self.named.data.xmat["torso"].reshape(3, 3) torso_pos = self.named.data.xpos["torso"] return -torso_pos.dot(torso_frame) def ball_state(self) -> np.ndarray: """Returns ball position and velocity relative to the torso frame.""" data = self.named.data torso_frame = data.xmat["torso"].reshape(3, 3) ball_rel_pos = data.xpos["ball"] - data.xpos["torso"] ball_rel_vel = data.qvel["ball_root"][:3] - data.qvel["root"][:3] ball_rot_vel = data.qvel["ball_root"][3:] ball_state = np.vstack((ball_rel_pos, ball_rel_vel, ball_rot_vel)) return ball_state.dot(torso_frame).ravel() def target_position(self) -> np.ndarray: """Returns target position in torso frame.""" torso_frame = self.named.data.xmat["torso"].reshape(3, 3) torso_pos = self.named.data.xpos["torso"] torso_to_target = self.named.data.site_xpos["target"] - torso_pos return torso_to_target.dot(torso_frame) def ball_to_target_distance(self) -> np.float64: """Returns horizontal distance from the ball to the target.""" ball_to_target = ( self.named.data.site_xpos["target"] - self.named.data.xpos["ball"] ) return np.linalg.norm(ball_to_target[:2]) def self_to_ball_distance(self) -> np.float64: """Returns horizontal distance from the quadruped workspace to the ball.""" self_to_ball = ( self.named.data.site_xpos["workspace"] - self.named.data.xpos["ball"] ) return np.linalg.norm(self_to_ball[:2]) def _find_non_contacting_height( physics: Physics, orientation: np.ndarray, x_pos: float = 0.0, y_pos: float = 0.0, ) -> None: """Find a height with no contacts given a body orientation. Args: physics: An instance of `Physics`. orientation: A quaternion. x_pos: A float. Position along global x-axis. y_pos: A float. Position along global y-axis. Raises: RuntimeError: If a non-contacting configuration has not been found after 10,000 attempts. """ z_pos = 0.0 # Start embedded in the floor. num_contacts = 1 num_attempts = 0 # Move up in 1cm increments until no contacts. while num_contacts > 0: try: with physics.reset_context(): physics.named.data.qpos["root"][:3] = x_pos, y_pos, z_pos physics.named.data.qpos["root"][3:] = orientation except control.PhysicsError: # We may encounter a PhysicsError here due to filling the contact # buffer, in which case we simply increment the height and continue. pass num_contacts = physics.data.ncon z_pos += 0.01 num_attempts += 1 if num_attempts > 10000: raise RuntimeError("Failed to find a non-contacting configuration.") def _common_observations(physics: Physics) -> collections.OrderedDict: """Returns the observations common to all tasks.""" obs = collections.OrderedDict() obs["egocentric_state"] = physics.egocentric_state() obs["torso_velocity"] = physics.torso_velocity() obs["torso_upright"] = physics.torso_upright() obs["imu"] = physics.imu() obs["force_torque"] = physics.force_torque() return obs def _upright_reward(physics: Physics, deviation_angle: float = 0) -> np.float64: """Returns a reward proportional to how upright the torso is. Args: physics: an instance of `Physics`. deviation_angle: A float, in degrees. The reward is 0 when the torso is exactly upside-down and 1 when the torso's z-axis is less than `deviation_angle` away from the global z-axis. """ deviation = np.cos(np.deg2rad(deviation_angle)) return rewards.tolerance( physics.torso_upright(), bounds=(deviation, float("inf")), sigmoid="linear", margin=1 + deviation, value_at_margin=0, ) class Move(base.Task): """A quadruped task solved by moving forward at a designated speed.""" def __init__( self, desired_speed: float, random: Union[np.random.RandomState, int, None] = None, ) -> None: """Initializes an instance of `Move`. Args: desired_speed: A float. If this value is zero, reward is given simply for standing upright. Otherwise this specifies the horizontal velocity at which the velocity-dependent reward component is maximized. random: Optional, either a `numpy.random.RandomState` instance, an integer seed for creating a new `RandomState`, or None to select a seed automatically (default). """ self._desired_speed = desired_speed super().__init__(random=random) def initialize_episode(self, physics: Physics) -> None: """Sets the state of the environment at the start of each episode. Args: physics: An instance of `Physics`. """ # Initial configuration. orientation = self.random.randn(4) orientation /= np.linalg.norm(orientation) _find_non_contacting_height(physics, orientation) super().initialize_episode(physics) def get_observation(self, physics: Physics) -> collections.OrderedDict: """Returns an observation to the agent.""" return _common_observations(physics) def get_reward(self, physics: Physics) -> np.float64: """Returns a reward to the agent.""" # Move reward term. move_reward = rewards.tolerance( physics.torso_velocity()[0], bounds=(self._desired_speed, float("inf")), margin=self._desired_speed, value_at_margin=0.5, sigmoid="linear", ) return _upright_reward(physics) * move_reward class Escape(base.Task): """A quadruped task solved by escaping a bowl-shaped terrain.""" def initialize_episode(self, physics: Physics) -> None: """Sets the state of the environment at the start of each episode. Args: physics: An instance of `Physics`. """ # Get heightfield resolution, assert that it is square. res = physics.model.hfield_nrow[_HEIGHTFIELD_ID] assert res == physics.model.hfield_ncol[_HEIGHTFIELD_ID] # Sinusoidal bowl shape. row_grid, col_grid = np.ogrid[-1 : 1 : res * 1j, -1 : 1 : res * 1j] radius = np.clip(np.sqrt(col_grid**2 + row_grid**2), 0.04, 1) bowl_shape = 0.5 - np.cos(2 * np.pi * radius) / 2 # Random smooth bumps. terrain_size = 2 * physics.model.hfield_size[_HEIGHTFIELD_ID, 0] bump_res = int(terrain_size / _TERRAIN_BUMP_SCALE) bumps = self.random.uniform(_TERRAIN_SMOOTHNESS, 1, (bump_res, bump_res)) smooth_bumps = ndimage.zoom(bumps, res / float(bump_res)) # Terrain is elementwise product. terrain = bowl_shape * smooth_bumps start_idx = physics.model.hfield_adr[_HEIGHTFIELD_ID] physics.model.hfield_data[start_idx : start_idx + res**2] = terrain.ravel() super().initialize_episode(physics) # If we have a rendering context, we need to re-upload the modified # heightfield data. if physics.contexts: with physics.contexts.gl.make_current() as ctx: ctx.call( mjlib.mjr_uploadHField, physics.model.ptr, physics.contexts.mujoco.ptr, _HEIGHTFIELD_ID, ) # Initial configuration. orientation = self.random.randn(4) orientation /= np.linalg.norm(orientation) _find_non_contacting_height(physics, orientation) def get_observation(self, physics: Physics) -> collections.OrderedDict: """Returns an observation to the agent.""" obs = _common_observations(physics) obs["origin"] = physics.origin() obs["rangefinder"] = physics.rangefinder() return obs def get_reward(self, physics: Physics) -> np.float64: """Returns a reward to the agent.""" # Escape reward term. terrain_size = physics.model.hfield_size[_HEIGHTFIELD_ID, 0] escape_reward = rewards.tolerance( physics.origin_distance(), bounds=(terrain_size, float("inf")), margin=terrain_size, value_at_margin=0, sigmoid="linear", ) return _upright_reward(physics, deviation_angle=20) * escape_reward class Fetch(base.Task): """A quadruped task solved by bringing a ball to the origin.""" def initialize_episode(self, physics: Physics) -> None: """Sets the state of the environment at the start of each episode. Args: physics: An instance of `Physics`. """ # Initial configuration, random azimuth and horizontal position. azimuth = self.random.uniform(0, 2 * np.pi) orientation = np.array((np.cos(azimuth / 2), 0, 0, np.sin(azimuth / 2))) spawn_radius = 0.9 * physics.named.model.geom_size["floor", 0] x_pos, y_pos = self.random.uniform(-spawn_radius, spawn_radius, size=(2,)) _find_non_contacting_height(physics, orientation, x_pos, y_pos) # Initial ball state. physics.named.data.qpos["ball_root"][:2] = self.random.uniform( -spawn_radius, spawn_radius, size=(2,) ) physics.named.data.qpos["ball_root"][2] = 2 physics.named.data.qvel["ball_root"][:2] = 5 * self.random.randn(2) super().initialize_episode(physics) def get_observation(self, physics: Physics) -> OrderedDict: """Returns an observation to the agent.""" obs = _common_observations(physics) obs["ball_state"] = physics.ball_state() obs["target_position"] = physics.target_position() return obs def get_reward(self, physics: Physics) -> np.float64: """Returns a reward to the agent.""" # Reward for moving close to the ball. arena_radius = physics.named.model.geom_size["floor", 0] * np.sqrt(2) workspace_radius = physics.named.model.site_size["workspace", 0] ball_radius = physics.named.model.geom_size["ball", 0] reach_reward = rewards.tolerance( physics.self_to_ball_distance(), bounds=(0, workspace_radius + ball_radius), sigmoid="linear", margin=arena_radius, value_at_margin=0, ) # Reward for bringing the ball to the target. target_radius = physics.named.model.site_size["target", 0] fetch_reward = rewards.tolerance( physics.ball_to_target_distance(), bounds=(0, target_radius), sigmoid="linear", margin=arena_radius, value_at_margin=0, ) reach_then_fetch = reach_reward * (0.5 + 0.5 * fetch_reward) return _upright_reward(physics) * reach_then_fetch
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CARL-main/carl/envs/dmc/dmc_tasks/utils.py
from __future__ import annotations from typing import List from lxml import etree # type: ignore from carl.utils.types import Context def adapt_context( xml_string: bytes, context: Context, context_mask: List = [] ) -> bytes: """Adapts and returns the xml_string of the model with the given context.""" def check_okay_to_set(context_feature: str | list[str]) -> bool: """Set context feature if present in context and not in context mask.""" is_okay: bool = True context_features: list[str] if type(context_feature) is str: context_features = [context_feature] # type: ignore[assignment] else: context_features = context_feature # type: ignore[assignment] for cf in context_features: if not (cf in context and cf not in context_mask): is_okay = False break return is_okay mjcf = etree.fromstring(xml_string) default = mjcf.find("./default/") if default is None: default = etree.Element("default") mjcf.addnext(default) if check_okay_to_set("joint_damping"): # adjust damping for all joints if damping is already an attribute for joint_find in mjcf.findall(".//joint[@damping]"): joint_damping = joint_find.get("damping") joint_find.set( "damping", str(float(joint_damping) * context["joint_damping"]) ) if check_okay_to_set("joint_stiffness"): # adjust stiffness for all joints if stiffness is already an attribute for joint_find in mjcf.findall(".//joint[@stiffness]"): joint_stiffness = joint_find.get("stiffness") joint_find.set( "stiffness", str(float(joint_stiffness) * context["joint_stiffness"]) ) # set default joint damping if default/joint is not present joint = mjcf.find("./default/joint") if joint is None: joint = etree.Element("joint") default.addnext(joint) if check_okay_to_set("joint_damping"): def_joint_damping = 0.1 default_joint_damping = str( float(def_joint_damping) * context["joint_damping"] ) joint.set("damping", default_joint_damping) if check_okay_to_set("joint_stiffness"): default_joint_stiffness = str(context["joint_stiffness"]) joint.set("stiffness", default_joint_stiffness) # adjust friction for all geom elements with friction attribute if check_okay_to_set( ["friction_tangential", "friction_torsional", "friction_rolling"] ): for geom_find in mjcf.findall(".//geom[@friction]"): friction = geom_find.get("friction").split(" ") frict_str = "" for i, (f, d) in enumerate( zip( friction, [ context["friction_tangential"], context["friction_torsional"], context["friction_rolling"], ], ) ): if ( (i == 0 and "friction_tangential" not in context_mask) or (i == 1 and "friction_torsional" not in context_mask) or (i == 2 and "friction_rolling" not in context_mask) ): frict_str += str(float(f) * d) + " " else: frict_str += str(f) + " " geom_find.set("friction", frict_str[:-1]) if check_okay_to_set("geom_density"): # adjust density for all geom elements with density attribute for geom_find in mjcf.findall(".//geom[@density]"): geom_find.set( "density", str(float(geom_find.get("density")) * context["geom_density"]), ) # create default geom if it does not exist geom = mjcf.find("./default/geom") if geom is None: geom = etree.Element("geom") default.addnext(geom) # set default friction if geom.get("friction") is None and check_okay_to_set( ["friction_tangential", "friction_torsional", "friction_rolling"] ): default_friction_tangential = 1.0 default_friction_torsional = 0.005 default_friction_rolling = 0.0001 geom.set( "friction", " ".join( [ ( str( default_friction_tangential * context["friction_tangential"] ) if "friction_tangential" not in context_mask else str(default_friction_tangential) ), ( str(default_friction_torsional * context["friction_torsional"]) if "friction_torsional" not in context_mask else str(default_friction_torsional) ), ( str(default_friction_rolling * context["friction_rolling"]) if "friction_rolling" not in context_mask else str(default_friction_rolling) ), ] ), ) if check_okay_to_set("geom_density"): # set default density geom_density = geom.get("density") if geom_density is None: geom_density = 1000 geom.set("density", str(float(geom_density) * context["geom_density"])) if check_okay_to_set("actuator_strength"): # scale all actuators with the actuator strength factor actuators = mjcf.findall("./actuator/") for actuator in actuators: gear = actuator.get("gear") if gear is None: gear = 1 actuator.set("gear", str(float(gear) * context["actuator_strength"])) # find option settings and override them if they exist, otherwise create new option option = mjcf.find(".//option") if option is None: option = etree.Element("option") mjcf.append(option) if check_okay_to_set("gravity"): gravity = option.get("gravity") if gravity is not None: g = gravity.split(" ") gravity = " ".join([g[0], g[1], str(context["gravity"])]) else: gravity = " ".join(["0", "0", str(context["gravity"])]) option.set("gravity", gravity) if check_okay_to_set("wind"): wind = option.get("wind") if wind is not None: w = wind.split(" ") wind = " ".join( [ (str(context["wind_x"]) if "wind_x" not in context_mask else w[0]), (str(context["wind_y"]) if "wind_y" not in context_mask else w[1]), (str(context["wind_z"]) if "wind_z" not in context_mask else w[2]), ] ) else: wind = " ".join( [ (str(context["wind_x"]) if "wind_x" not in context_mask else "0"), (str(context["wind_y"]) if "wind_y" not in context_mask else "0"), (str(context["wind_z"]) if "wind_z" not in context_mask else "0"), ] ) option.set("wind", wind) if check_okay_to_set("timestep"): option.set("timestep", str(context["timestep"])) if check_okay_to_set("density"): option.set("density", str(context["density"])) if check_okay_to_set("viscosity"): option.set("viscosity", str(context["viscosity"])) xml_string = etree.tostring(mjcf, pretty_print=True) return xml_string
7,735
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CARL-main/carl/envs/dmc/dmc_tasks/walker.py
# Copyright 2017 The dm_control Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Planar Walker Domain.""" from typing import Dict, List, Optional, Tuple, Union import collections import dm_env # type: ignore import numpy as np from dm_control import mujoco # type: ignore from dm_control.rl import control # type: ignore from dm_control.suite import base, common # type: ignore from dm_control.suite.utils import randomizers # type: ignore from dm_control.utils import containers, rewards # type: ignore from carl.envs.dmc.dmc_tasks.utils import adapt_context # type: ignore from carl.utils.types import Context _DEFAULT_TIME_LIMIT = 25 _CONTROL_TIMESTEP = 0.025 # Minimal height of torso over foot above which stand reward is 1. _STAND_HEIGHT = 1.2 # Horizontal speeds (meters/second) above which move reward is 1. _WALK_SPEED = 1 _RUN_SPEED = 8 STEP_LIMIT = 1000 SUITE = containers.TaggedTasks() def get_model_and_assets() -> Tuple[bytes, Dict]: """Returns a tuple containing the model XML string and a dict of assets.""" return common.read_model("walker.xml"), common.ASSETS @SUITE.add("benchmarking") # type: ignore def stand_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Stand task with the adapted context.""" xml_string, assets = get_model_and_assets() if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = PlanarWalker(move_speed=0, random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) @SUITE.add("benchmarking") # type: ignore def walk_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Walk task with the adapted context.""" xml_string, assets = get_model_and_assets() if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = PlanarWalker(move_speed=_WALK_SPEED, random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) @SUITE.add("benchmarking") # type: ignore def run_context( context: Context = {}, context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, ) -> dm_env: """Returns the Run task with the adapted context.""" xml_string, assets = get_model_and_assets() if context != {}: xml_string = adapt_context( xml_string=xml_string, context=context, context_mask=context_mask ) physics = Physics.from_xml_string(xml_string, assets) task = PlanarWalker(move_speed=_RUN_SPEED, random=random) environment_kwargs = environment_kwargs or {} return control.Environment( physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP, **environment_kwargs, ) class Physics(mujoco.Physics): """Physics simulation with additional features for the Walker domain.""" def torso_upright(self) -> np.float64: """Returns projection from z-axes of torso to the z-axes of world.""" return self.named.data.xmat["torso", "zz"] def torso_height(self) -> np.float64: """Returns the height of the torso.""" return self.named.data.xpos["torso", "z"] def horizontal_velocity(self) -> np.float64: """Returns the horizontal velocity of the center-of-mass.""" return self.named.data.sensordata["torso_subtreelinvel"][0] def orientations(self) -> np.ndarray: """Returns planar orientations of all bodies.""" return self.named.data.xmat[1:, ["xx", "xz"]].ravel() class PlanarWalker(base.Task): """A planar walker task.""" def __init__( self, move_speed: float, random: Union[np.random.RandomState, int, None] = None ) -> None: """Initializes an instance of `PlanarWalker`. Args: move_speed: A float. If this value is zero, reward is given simply for standing up. Otherwise this specifies a target horizontal velocity for the walking task. random: Optional, either a `numpy.random.RandomState` instance, an integer seed for creating a new `RandomState`, or None to select a seed automatically (default). """ self._move_speed = move_speed super().__init__(random=random) def initialize_episode(self, physics: Physics) -> None: """Sets the state of the environment at the start of each episode. In 'standing' mode, use initial orientation and small velocities. In 'random' mode, randomize joint angles and let fall to the floor. Args: physics: An instance of `Physics`. """ randomizers.randomize_limited_and_rotational_joints(physics, self.random) super().initialize_episode(physics) def get_observation(self, physics: Physics) -> collections.OrderedDict: """Returns an observation of body orientations, height and velocites.""" obs = collections.OrderedDict() obs["orientations"] = physics.orientations() obs["height"] = physics.torso_height() # type: ignore obs["velocity"] = physics.velocity() self.get_reward(physics) return obs def get_reward(self, physics: Physics) -> np.float64: """Returns a reward to the agent.""" standing = rewards.tolerance( physics.torso_height(), bounds=(_STAND_HEIGHT, float("inf")), margin=_STAND_HEIGHT / 2, ) upright = (1 + physics.torso_upright()) / 2 stand_reward = (3 * standing + upright) / 4 if self._move_speed == 0: return stand_reward else: move_reward = rewards.tolerance( physics.horizontal_velocity(), bounds=(self._move_speed, float("inf")), margin=self._move_speed / 2, value_at_margin=0.5, sigmoid="linear", ) return stand_reward * (5 * move_reward + 1) / 6
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CARL-main/carl/envs/mario/__init__.py
# flake8: noqa: F401 import warnings try: from carl.envs.mario.carl_mario import CARLMarioEnv except Exception as e: warnings.warn(f"Could not load CARLMarioEnv which is probably not installed ({e}).") from carl.envs.mario.carl_mario_definitions import CONTEXT_BOUNDS as CARLMarioEnv_bounds from carl.envs.mario.carl_mario_definitions import ( DEFAULT_CONTEXT as CARLMarioEnv_defaults, )
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CARL-main/carl/envs/mario/carl_mario.py
from typing import Dict, List, Optional, Union import gym from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.envs.mario.carl_mario_definitions import ( DEFAULT_CONTEXT, INITIAL_HEIGHT, INITIAL_WIDTH, ) from carl.envs.mario.mario_env import MarioEnv from carl.envs.mario.toad_gan import generate_level from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts class CARLMarioEnv(CARLEnv): def __init__( self, env: gym.Env = MarioEnv(levels=[]), contexts: Contexts = {}, hide_context: bool = True, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.05, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, ): if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( env=env, contexts=contexts, hide_context=True, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features="no", default_context=default_context, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.levels: List[str] = [] self._update_context() def _update_context(self) -> None: self.env: MarioEnv if not self.levels: for context in self.contexts.values(): level = generate_level( width=INITIAL_WIDTH, height=INITIAL_HEIGHT, level_index=context["level_index"], initial_noise=context["noise"], filter_unplayable=True, ) self.levels.append(level) self.env.mario_state = self.context["mario_state"] self.env.mario_inertia = self.context["mario_inertia"] self.env.levels = [self.levels[self.context_index]] def _log_context(self) -> None: if self.logger: loggable_context = {k: v for k, v in self.context.items() if k != "noise"} self.logger.write_context( self.episode_counter, self.total_timestep_counter, loggable_context )
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CARL-main/carl/envs/mario/carl_mario_definitions.py
import numpy as np from torch import Tensor try: from carl.envs.mario.toad_gan import generate_initial_noise except FileNotFoundError: def generate_initial_noise(width: int, height: int, level_index: int) -> Tensor: return Tensor() INITIAL_WIDTH = 100 INITIAL_LEVEL_INDEX = 0 INITIAL_HEIGHT = 16 DEFAULT_CONTEXT = { "level_index": INITIAL_LEVEL_INDEX, "noise": generate_initial_noise(INITIAL_WIDTH, INITIAL_HEIGHT, INITIAL_LEVEL_INDEX), "mario_state": 0, "mario_inertia": 0.89, } CONTEXT_BOUNDS = { "level_index": (None, None, "categorical", np.arange(0, 14)), "noise": (-1.0, 1.0, float), "mario_state": (None, None, "categorical", [0, 1, 2]), "mario_inertia": (0.5, 1.5, float), } CATEGORICAL_CONTEXT_FEATURES = ["level_index", "mario_state"]
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CARL-main/carl/envs/mario/generate_sample.py
# Code from https://github.com/Mawiszus/TOAD-GAN from typing import Any, List, Optional, Tuple, Union import torch import torch.nn as nn from torch import Tensor from torch.nn.functional import interpolate # Generates a noise tensor. Uses torch.randn. def generate_spatial_noise( size: Union[Any, List[int], Tuple[int]], device: Union[str, torch.device] = "cpu" ) -> Tensor: return torch.randn(size, device=device, dtype=torch.float32) # Generate a sample given a TOAD-GAN and additional parameters @torch.no_grad() # type: ignore [misc] def generate_sample( generators: Tensor, noise_maps: Tensor, reals: Tensor, noise_amplitudes: Tensor, num_layer: int, token_list: Tensor, scale_v: float = 1.0, scale_h: float = 1.0, current_scale: int = 0, gen_start_scale: int = 0, initial_noise: Optional[Tensor] = None, ) -> List[str]: in_s = None images_cur: List[Tensor] = [] images: List[Tensor] = [] z_s: List[Tensor] = [] # Generate on GPU if available device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Main loop for G, Z_opt, noise_amp in zip(generators, noise_maps, noise_amplitudes): if current_scale >= len(generators): break # should not be reached # Zero Padding n_pad = int(num_layer) m = nn.ZeroPad2d(int(n_pad)) # Calculate actual shape nzx = (Z_opt.shape[2] - n_pad * 2) * scale_v nzy = (Z_opt.shape[3] - n_pad * 2) * scale_h # Init images list images_prev = images_cur images_cur = [] channels = len(token_list) # Init in_s if in_s is None: in_s = torch.zeros(reals[0].shape[0], channels, *reals[0].shape[2:]).to( device ) elif in_s.sum() == 0: in_s = torch.zeros(in_s.shape[0], channels, *in_s.shape[2:]).to(device) if current_scale == 0: # First step: Make base noise if initial_noise is not None and len(initial_noise) > 0: z_curr = initial_noise.float().to(device) else: z_curr = generate_spatial_noise( [1, channels, int(round(nzx)), int(round(nzy))], device=device ) z_curr = m(z_curr) else: # All other steps: Make added noise if current_scale < gen_start_scale: z_curr = z_s[current_scale] else: z_curr = generate_spatial_noise( [1, channels, int(round(nzx)), int(round(nzy))], device=device ) z_curr = m(z_curr) if (not images_prev) or current_scale == 0: # if there is no "previous" image I_prev = in_s else: I_prev = images[current_scale - 1] # Bilinear interpolation for upscaling I_prev = interpolate( I_prev, [int(round(nzx)), int(round(nzy))], mode="bilinear", align_corners=False, ) I_prev = m(I_prev) # Main Step z_in = noise_amp * z_curr + I_prev I_curr = G(z_in, I_prev, temperature=1) # Append results images_cur.append(I_curr) if current_scale >= gen_start_scale: images.append(I_curr) z_s.append(z_curr) current_scale += 1 return one_hot_to_ascii_level(I_curr, token_list) def one_hot_to_ascii_level(level: Any, tokens: Any) -> List[str]: """Converts a full token level tensor to an ascii level.""" ascii_level = [] for i in range(level.shape[2]): line = "" for j in range(level.shape[3]): line += tokens[level[:, :, i, j].argmax()] if i < level.shape[2] - 1: line += "\n" ascii_level.append(line) return ascii_level
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CARL-main/carl/envs/mario/level_image_gen.py
# Code from https://github.com/Mawiszus/TOAD-GAN from typing import Any, List, Tuple import os from PIL import Image, ImageEnhance, ImageOps class LevelImageGen: """Generates PIL Image files from Super Mario Bros. ascii levels. Initialize once and then use LevelImageGen.render() to generate images.""" def __init__(self, sprite_path: str): """sprite_path: path to the folder of sprite files, e.g. 'mario/sprites/'""" # Load Graphics (assumes sprite_path points to "img" folder of Mario-AI-Framework or provided sprites folder mariosheet = Image.open(os.path.join(sprite_path, "smallmariosheet.png")) enemysheet = Image.open(os.path.join(sprite_path, "enemysheet.png")) itemsheet = Image.open(os.path.join(sprite_path, "itemsheet.png")) mapsheet = Image.open(os.path.join(sprite_path, "mapsheet.png")) # Cut out the actual sprites: sprite_dict = dict() # Mario Sheet sprite_dict["M"] = mariosheet.crop((4 * 16, 0, 5 * 16, 16)) # Enemy Sheet enemy_names = ["r", "k", "g", "y", "wings", "*", "plant"] for i, e in enumerate(enemy_names): sprite_dict[e] = enemysheet.crop((0, i * 2 * 16, 16, (i + 1) * 2 * 16)) sprite_dict["E"] = enemysheet.crop( (16, 2 * 2 * 16, 2 * 16, 3 * 2 * 16) ) # Set generic enemy to second goomba sprite sprite_dict["plant"] = enemysheet.crop( (16, (len(enemy_names) - 1) * 2 * 16, 2 * 16, len(enemy_names) * 2 * 16) ) # Item Sheet sprite_dict["shroom"] = itemsheet.crop((0, 0, 16, 16)) sprite_dict["flower"] = itemsheet.crop((16, 0, 2 * 16, 16)) sprite_dict["flower2"] = itemsheet.crop((0, 16, 16, 2 * 16)) sprite_dict["1up"] = itemsheet.crop((16, 16, 2 * 16, 2 * 16)) # Map Sheet map_names = [ "-", "X", "#", "B", "b", "b2", "S", "L", "?", "dump", "@", "Q", "dump", "!", "D", "o", "o2", "o3", "<", ">", "[", "]", "bg_sl_l", "bg_top", "bg_sl_r", "bg_m_l", "bg_m", "bg_m_r", "bush_l", "bush_m", "bush_r", "cloud_l", "cloud_m", "cloud_r", "cloud_b_l", "cloud_b_m", "cloud_b_r", "waves", "water", "F_top", "F_b", "F", "bg_sky", "%", "%_l", "%_r", "%_m", "|", "1", "2", "C", "U", "T", "t", "dump", "dump", ] sheet_length = (7, 8) sprite_counter = 0 for i in range(sheet_length[0]): for j in range(sheet_length[1]): sprite_dict[map_names[sprite_counter]] = mapsheet.crop( (j * 16, i * 16, (j + 1) * 16, (i + 1) * 16) ) sprite_counter += 1 sprite_dict["@"] = sprite_dict["?"] sprite_dict["!"] = sprite_dict["Q"] self.sprite_dict = sprite_dict def prepare_sprite_and_box( self, ascii_level: List[str], sprite_key: str, curr_x: int, curr_y: int ) -> Tuple[Any, Tuple[int, int, int, int]]: """Helper to make correct sprites and sprite sizes to draw into the image. Some sprites are bigger than one tile and the renderer needs to adjust for them.""" # Init default size new_left = curr_x * 16 new_top = curr_y * 16 new_right = (curr_x + 1) * 16 new_bottom = (curr_y + 1) * 16 # Handle sprites depending on their type: if sprite_key == "F": # Flag Pole actual_sprite = Image.new("RGBA", (2 * 16, curr_y * 16)) actual_sprite.paste(self.sprite_dict["F_top"], (16, 0, 2 * 16, 16)) for s in range(curr_y): actual_sprite.paste( self.sprite_dict["F_b"], (16, (s + 1) * 16, 2 * 16, (s + 2) * 16) ) actual_sprite.paste(self.sprite_dict["F"], (7, 1 * 16, 16 + 7, 2 * 16)) new_left = new_left - 16 new_top = new_top - (curr_y - 1) * 16 elif sprite_key in ["y", "E", "g", "k", "r"]: # enemy sprite actual_sprite = self.sprite_dict[sprite_key] new_top = new_top - 16 elif sprite_key in ["Y", "K", "R"]: # winged spiky/koopa sprite actual_sprite = Image.new("RGBA", (2 * 16, 2 * 16)) actual_sprite.paste( self.sprite_dict[str.lower(sprite_key)], (16, 0, 2 * 16, 2 * 16) ) actual_sprite.paste(self.sprite_dict["wings"], (7, -7, 16 + 7, 2 * 16 - 7)) new_left = new_left - 16 new_top = new_top - 16 elif ( sprite_key == "G" ): # winged goomba sprite (untested because original has none?) actual_sprite = Image.new("RGBA", (3 * 16, 2 * 16)) actual_sprite.paste(self.sprite_dict["wings"], (1, -5, 16 + 1, 2 * 16 - 5)) actual_sprite.paste( ImageOps.mirror(self.sprite_dict["wings"]), (2 * 16 - 1, -5, 3 * 16 - 1, 2 * 16 - 5), ) actual_sprite.paste( self.sprite_dict[str.lower(sprite_key)], (16, 0, 2 * 16, 2 * 16) ) new_left = new_left - 16 new_top = new_top - 16 new_right = new_right + 16 elif sprite_key == "%": # jump through platform if curr_x == 0: if ( len(ascii_level[curr_y]) > 1 and ascii_level[curr_y][curr_x + 1] == sprite_key ): # middle piece actual_sprite = self.sprite_dict["%_m"] else: # single_piece actual_sprite = self.sprite_dict["%"] elif ascii_level[curr_y][curr_x - 1] == sprite_key: if curr_x >= (len(ascii_level[curr_y]) - 1): # right end piece actual_sprite = self.sprite_dict["%_r"] elif ascii_level[curr_y][curr_x + 1] == sprite_key: # middle piece actual_sprite = self.sprite_dict["%_m"] else: # right end piece actual_sprite = self.sprite_dict["%_r"] else: if curr_x >= (len(ascii_level[curr_y]) - 1): # single piece actual_sprite = self.sprite_dict["%"] elif ascii_level[curr_y][curr_x + 1] == sprite_key: # left end piece actual_sprite = self.sprite_dict["%_l"] else: # single piece actual_sprite = self.sprite_dict[sprite_key] elif sprite_key == "b": # bullet bill tower if curr_y > 0: if ascii_level[curr_y - 1][curr_x] == sprite_key: actual_sprite = self.sprite_dict["b2"] else: actual_sprite = self.sprite_dict[sprite_key] else: actual_sprite = self.sprite_dict[sprite_key] elif sprite_key == "*": # alternative bullet bill tower if curr_y > 0: if ascii_level[curr_y - 1][curr_x] != sprite_key: # top actual_sprite = self.sprite_dict["B"] elif curr_y > 1: if ascii_level[curr_y - 2][curr_x] != sprite_key: actual_sprite = self.sprite_dict["b"] else: actual_sprite = self.sprite_dict["b2"] else: actual_sprite = self.sprite_dict["b2"] elif sprite_key in ["T", "t"]: # Pipes # figure out what kind of pipe this is if curr_y > 0 and ascii_level[curr_y - 1][curr_x] == sprite_key: is_top = False else: is_top = True pipelength_t = 0 while ( curr_y - pipelength_t >= 0 and ascii_level[curr_y - pipelength_t][curr_x] == sprite_key ): pipelength_t += 1 pipelength_b = 0 while ( curr_y + pipelength_b < len(ascii_level) and ascii_level[curr_y + pipelength_b][curr_x] == sprite_key ): pipelength_b += 1 pipelength_l = 0 while ( curr_x - pipelength_l >= 0 and ascii_level[curr_y][curr_x - pipelength_l] == sprite_key ): pipelength_l += 1 pipelength_r = 0 while ( curr_x + pipelength_r < len(ascii_level[curr_y]) and ascii_level[curr_y][curr_x - pipelength_r] == sprite_key ): pipelength_r += 1 # Check for fall out criteria try: if pipelength_l % 2 == 0: # second half of a double pipe is_left = False is_right = True elif pipelength_l % 2 == 1: if ( curr_x >= len(ascii_level[curr_y]) or ascii_level[curr_y][curr_x + 1] != sprite_key ): is_left = False is_right = False else: is_left = True is_right = False else: is_left = False is_right = False if is_left: if ascii_level[curr_y - pipelength_t][curr_x + 1] == sprite_key: is_left = False is_right = False if ascii_level[curr_y - pipelength_t + 1][curr_x + 1] != sprite_key: is_left = False is_right = False if is_right: if ascii_level[curr_y - pipelength_t][curr_x - 1] == sprite_key: is_left = False is_right = False if ascii_level[curr_y - pipelength_t + 1][curr_x - 1] != sprite_key: is_left = False is_right = False if curr_y + pipelength_b < len(ascii_level): if is_left: if ascii_level[curr_y + pipelength_b][curr_x + 1] == sprite_key: is_left = False is_right = False if ( ascii_level[curr_y + pipelength_b - 1][curr_x + 1] != sprite_key ): is_left = False is_right = False if is_right: if ascii_level[curr_y + pipelength_b][curr_x - 1] == sprite_key: is_left = False is_right = False if ( ascii_level[curr_y + pipelength_b - 1][curr_x - 1] != sprite_key ): is_left = False is_right = False except IndexError: # Default to single pipe is_left = False is_right = False if is_top: if is_left: actual_sprite = self.sprite_dict["<"] elif is_right: if sprite_key == "T": actual_sprite = Image.new("RGBA", (2 * 16, 3 * 16)) actual_sprite.paste( self.sprite_dict["plant"], (8, 5, 16 + 8, 2 * 16 + 5) ) actual_sprite.paste( self.sprite_dict["<"], (0, 2 * 16, 16, 3 * 16) ) actual_sprite.paste( self.sprite_dict[">"], (16, 2 * 16, 2 * 16, 3 * 16) ) new_left = new_left - 16 new_top = new_top - 2 * 16 else: actual_sprite = self.sprite_dict[">"] else: if sprite_key == "T": actual_sprite = Image.new("RGBA", (16, 3 * 16)) actual_sprite.paste( self.sprite_dict["plant"], (0, 5, 16, 2 * 16 + 5) ) actual_sprite.paste( self.sprite_dict["T"], (0, 2 * 16, 16, 3 * 16) ) new_top = new_top - 2 * 16 else: actual_sprite = self.sprite_dict["T"] else: if is_left: actual_sprite = self.sprite_dict["["] elif is_right: actual_sprite = self.sprite_dict["]"] else: actual_sprite = self.sprite_dict["t"] elif sprite_key in [ "?", "@", "Q", "!", "C", "U", "L", ]: # Block/Brick hidden items if sprite_key == "L": i_key = "1up" elif sprite_key in ["?", "@", "U"]: i_key = "shroom" else: i_key = "o" mask = self.sprite_dict[i_key].getchannel(3) mask = ImageEnhance.Brightness(mask).enhance(0.7) actual_sprite = Image.composite( self.sprite_dict[i_key], self.sprite_dict[sprite_key], mask=mask ) elif sprite_key in ["1", "2"]: # Hidden block if sprite_key == "1": i_key = "1up" else: i_key = "o" mask1 = self.sprite_dict["D"].getchannel(3) mask1 = ImageEnhance.Brightness(mask1).enhance(0.5) tmp_sprite = Image.composite( self.sprite_dict["D"], self.sprite_dict[sprite_key], mask=mask1 ) mask = self.sprite_dict[i_key].getchannel(3) mask = ImageEnhance.Brightness(mask).enhance(0.7) actual_sprite = Image.composite( self.sprite_dict[i_key], tmp_sprite, mask=mask ) else: actual_sprite = self.sprite_dict[sprite_key] return actual_sprite, (new_left, new_top, new_right, new_bottom) def render(self, ascii_level: List[str]) -> Image: """Renders the ascii level as a PIL Image. Assumes the Background is sky""" len_level = len(ascii_level[-1]) height_level = len(ascii_level) # Fill base image with sky tiles dst = Image.new("RGB", (len_level * 16, height_level * 16)) for y in range(height_level): for x in range(len_level): dst.paste( self.sprite_dict["bg_sky"], (x * 16, y * 16, (x + 1) * 16, (y + 1) * 16), ) # Fill with actual tiles for y in range(height_level): for x in range(len_level): curr_sprite = ascii_level[y][x] sprite, box = self.prepare_sprite_and_box( ascii_level, curr_sprite, x, y ) dst.paste(sprite, box, mask=sprite) return dst
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CARL-main/carl/envs/mario/mario_env.py
from typing import Any, ByteString, Deque, Dict, List, Literal, Optional, Union, cast import os import random import socket from collections import deque import cv2 import gym import numpy as np from gym import spaces from gym.core import ObsType from gym.utils import seeding from PIL import Image from py4j.java_gateway import GatewayParameters, JavaGateway from carl.envs.mario.level_image_gen import LevelImageGen from .mario_game import MarioGame from .utils import get_port, load_level class MarioEnv(gym.Env): metadata = {"render.modes": ["rgb_array"]} def __init__( self, levels: List[str], timer: int = 100, visual: bool = False, sticky_action_probability: float = 0.1, frame_skip: int = 2, frame_stack: int = 4, frame_dim: int = 64, hide_points_banner: bool = False, sparse_rewards: bool = False, grayscale: bool = False, seed: int = 0, ): self.gateway: Any = None self.seed(seed) self.level_names = levels self.levels = [load_level(name) for name in levels] self.timer = timer self.visual = visual self.frame_skip = frame_skip self.frame_stack = frame_stack self.sticky_action_probability = sticky_action_probability self.hide_points_banner = hide_points_banner self.sparse_rewards = sparse_rewards self.points_banner_height = 4 self.grayscale = grayscale self.last_action = None self.width = self.height = frame_dim self.observation_space = spaces.Box( low=0, high=255, shape=[self.frame_stack if grayscale else 3, self.height, self.width], dtype=np.uint8, ) self.original_obs: Deque = deque(maxlen=self.frame_skip) self.actions = [ [False, False, False, False, False], # noop [False, False, True, False, False], # down [False, True, False, False, False], # right [False, True, False, True, False], # right speed [False, True, False, False, True], # right jump [False, True, False, True, True], # right speed jump [True, False, False, False, False], # left [True, False, False, False, True], # left jump [True, False, False, True, True], # left speed jump [False, False, False, False, True], # jump ] self.action_space = spaces.Discrete(n=len(self.actions)) self._obs: Any = np.zeros(shape=self.observation_space.shape, dtype=np.uint8) self.current_level_idx = 0 self.frame_size = -1 self.port = get_port() self.mario_state: Literal[0, 1, 2] = 0 # normal, large, fire self.mario_inertia = 0.89 self._init_game() def reset( self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None, ) -> Union[ObsType, tuple[ObsType, dict]]: self._reset_obs() if self.game is None: self.game: Any = self._init_game() self.current_level_idx = (self.current_level_idx + 1) % len(self.levels) level = self.levels[self.current_level_idx] self.game.resetGame(level, self.timer, self.mario_state, self.mario_inertia) self.game.computeObservationRGB() buffer = self._receive() frame = self._read_frame(buffer) self._update_obs(frame) if not return_info: return self._obs.copy() else: return self._obs.copy(), {} def step(self, action: Any) -> Any: if self.sticky_action_probability != 0.0: if ( self.last_action is not None and random.random() < self.sticky_action_probability ): a = self.actions[self.last_action] else: a = self.actions[action] self.last_action = action else: a = self.actions[action] assert self.game frame = None for i in range(self.frame_skip): self.game.stepGame(*a) if self.visual or i == self.frame_skip - 1: self.game.computeObservationRGB() buffer = self._receive() frame = self._read_frame(buffer) self._update_obs(frame) reward, done, completionPercentage = ( self.game.computeReward(), self.game.computeDone(), self.game.getCompletionPercentage(), ) info: Dict[str, Any] = {"completed": completionPercentage} if self.visual: info["original_obs"] = self.original_obs return ( self._obs.copy(), reward if not self.sparse_rewards else int(completionPercentage == 1.0), done, # bool info, # Dict[str, Any] ) def render(self, *args: Any, **kwargs: Any) -> ObsType: return self.original_obs[0] def __getstate__(self) -> Dict: assert self.gateway self.gateway.close() self.gateway = None self.game = None self.socket.shutdown(1) self.socket.close() return self.__dict__ def _reset_obs(self) -> None: self._obs[:] = 0 self.original_obs.clear() def _read_frame(self, buffer: Any) -> Any: frame = ( np.frombuffer(buffer, dtype=np.int32).reshape(256, 256, 3).astype(np.uint8) ) self.original_obs.append(frame) return frame def _update_obs(self, frame: Any) -> Any: if self.grayscale: frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) frame = cv2.resize(frame, (self.width, self.height), cv2.INTER_NEAREST) if self.hide_points_banner: frame[: self.points_banner_height, :] = 0 if self.grayscale: self._obs = np.concatenate([self._obs[1:], frame[np.newaxis]]) else: self._obs = np.transpose(frame, axes=(2, 0, 1)) def _init_game(self) -> MarioGame: self.gateway = JavaGateway( gateway_parameters=GatewayParameters( port=self.port, eager_load=True, ) ) self.game = cast(MarioGame, cast(Any, self.gateway.jvm).engine.core.MarioGame()) self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.socket.connect(("localhost", self.game.getPort())) self.game.initGame() self.frame_size = self.game.getFrameSize() return self.game def _receive(self) -> ByteString: frameBuffer = b"" while len(frameBuffer) != self.frame_size: frameBuffer += self.socket.recv(self.frame_size) return frameBuffer def get_action_meanings(self) -> List[str]: return ACTION_MEANING def render_current_level(self) -> Image: img_gen = LevelImageGen( sprite_path=os.path.abspath( os.path.join(os.path.dirname(__file__), "sprites") ) ) return img_gen.render(self.levels[self.current_level_idx].split("\n")) def seed(self, seed: Optional[int] = None) -> List[Any]: self.np_random, seed = seeding.np_random(seed) return [seed] ACTION_MEANING = [ "NOOP", "DOWN", "RIGHT", "RIGHTSPEED", "RIGHTJUMP", "RIGHTSPEEDJUMP", "LEFT", "LEFTJUMP", "LEFTSPEEDJUMP", "JUMP", ]
7,520
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CARL-main/carl/envs/mario/mario_game.py
from abc import ABC, abstractmethod class MarioGame(ABC): @abstractmethod def getPort(self) -> int: pass @abstractmethod def initGame(self) -> None: pass @abstractmethod def stepGame( self, left: bool, right: bool, down: bool, speed: bool, jump: bool ) -> None: pass @abstractmethod def resetGame( self, level: str, timer: int, mario_state: int, inertia: float ) -> None: pass @abstractmethod def computeObservationRGB(self) -> None: pass @abstractmethod def computeReward(self) -> float: pass @abstractmethod def computeDone(self) -> bool: pass @abstractmethod def getCompletionPercentage(self) -> float: pass @abstractmethod def getFrameSize(self) -> int: pass
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CARL-main/carl/envs/mario/reachabillity.py
from typing import List, Tuple import numpy as np horizontal = 10 # when sprinting Mario can jump over a 10 tiles gap horizontally vertical = 4 # Mario can jump over a 4 tile wall diagonal = ( 6 # Mario can jump over 6 tiles to clear a 4 block height difference when sprinting ) empty = "-" ignored = ["M", "F", "|", "E", "g", "k", "r", "y", "G", "K", "R", "Y", "*", "B", "o"] def remove_ignored(level: List[str]) -> Tuple[List, Tuple[int, int], Tuple[int, int]]: """ Replaces all ignored tokens with the empty token in a level. In case of Mario and the flag the coordinates of the blocks below are returned and they are also replaced. :param level: a level in ASCII form :return: the level in ASCII form with the ignored tokens replaced and the coordinates of the block below Mario and the flag if existing """ new_level = [] mario = (-1, -1) flag = (-1, -1) for i, row in enumerate(level): mario_y = row.find("M") if mario_y >= 0: mario = (i + 1, mario_y) flag_y = row.find("F") if flag_y >= 0: flag = (i + 1, flag_y) for token in ignored: row = row.replace(token, empty) new_level.append(row) return new_level, mario, flag def reachability_map( level: List[str], shape: Tuple[int, int], has_mario: bool = False, has_flag: bool = False, check_outside: bool = False, ) -> Tuple[np.ndarray, bool]: """ This creates a numpy 2D array containing the reachability map for a given ASCII-Level. Every solid block will have a 1 if Mario can stand on it and can reach the tile and a 0 else. Currently ignoring sprint. Levels are generated without Mario and the flag and as such the algorithm is not including these. :param level: The level (slice) as a list containing the ASCII strings of each level row :param shape: The level shape :param has_mario: As levels are expected to be generated without Mario, this option has to be set to search for Mario as a starting point :param has_flag: As levels are expected to be generated without the flag, this option has to be set to determine playability via reaching the flag :param check_outside: If this option is set, playability will check if the player can reach the outside :return: A numpy array where a 0 indicates an unreachable block and a 1 denotes a reachable block; a boolean indicating if the level can be finished by the player """ level, mario, flag = remove_ignored(level) reachability_map = np.zeros(shape=shape) index_queue = [] # find the starting point, either the block Mario is standing on or the first solid block Mario could stand on found_first = False if has_mario: index_queue.append(mario) else: for i in range(shape[0] - 1, 0, -1): # start from the bottom of the level for j in range(0, shape[1]): tile = level[i][j] if ( tile != empty and ( reachability_map[i][j] == 1 or not found_first and i < shape[0] - 1 ) and i > 0 and level[i - 1][j] == empty ): found, queue, _ = mark(level, reachability_map, i, j) index_queue.extend(queue) if not found_first: found_first = found break if found_first: break # calculate all reachable positions by applying a BFS type of algorithm outside = False while len(index_queue) > 0: index = index_queue.pop() _, queue, reached_outside = mark( level, reachability_map, index[0], index[1], check_outside=check_outside ) if reached_outside: outside = True index_queue.extend(queue) # a level is playable if either the flag is reachable or if no flag is included, the rightmost side can be reached # Bug: if the level ends with a gap, it might be playable but still wouldn't count as such playable = False if has_flag: if reachability_map[flag[0]][flag[1]]: playable = True else: # look at all tiles in the last column for i in range(1, shape[0]): if reachability_map[shape[0] - i][shape[1] - 1]: playable = True break if not playable and check_outside: # Assumption is that reaching the outside is identical to completing the level if outside: playable = True return reachability_map, playable def check_blocked( level: List[str], i: int, j: int, dh: int, dv: int, right: bool ) -> int: """ Checks for a given position, level and direction if a blockade exists in the range specified by dh and dv. :param level: The level in ASCII form :param i: x coordinate of the starting position :param j: y coordinate of the starting position :param dh: amount of blocks in the horizontal direction from the starting point the algorithm tries to jump :param dv: amount of blocks in the vertical direction from the starting point the algorithm tries to jump :param right: direction of the jump :return: the blockade y value if a blockade is found, default max value otherwise """ blocked = horizontal + 1 # default value boundary = j + dh if right else j - dh try: if level[i - dv][boundary] != empty: height = 1 + dv while height < vertical + 1: v = i - dv - height if v < 0: # over maximum level height, cannot pass blocked = dh break if level[v][boundary] != empty or dh + height > 10: height += 1 else: break if height == vertical + 1: blocked = dh except IndexError: # over maximum level height, cannot pass blocked = dh return blocked def check_down( level: List[str], map: np.ndarray, i: int, j: int, dh: int, check_outside: bool, right: bool, ) -> Tuple[bool, bool, List[Tuple[int, int]]]: drop = 1 found_first = False reach_outside = False found = [] boundary = j + dh if right else j - dh if boundary > map.shape[1] - 1: if check_outside: reach_outside = True else: y = min(max(boundary, 0), map.shape[1] - 1) while i + drop < map.shape[0]: # right and down x = i + drop above = x - 1 if ( level[x][y] != empty and above >= 0 and level[above][y] == empty and map[x][y] != 1 ): map[x][y] = 1 found.append((x, y)) found_first = True break drop += 1 return found_first, reach_outside, found def mark( level: List[str], reachability_map: np.ndarray, i: int, j: int, check_outside: bool = False, ) -> Tuple[bool, List[Tuple[int, int]], bool]: """ For a given position and a level this will mark all tiles reachable from the given position and collect all these positions for further use. :param level: The level (slice) as a list containing the ASCII strings of each level row :param map: The current reachability map where the reachable tiles will be marked :param i: x coordinate :param j: y coordinate :param check_outside: if the algorithm should indicate that the player can reach the right outside of the level :return: A boolean indicating if any tile can be reached from this position, a list of all reachable positions and if the outside can be reached """ found_first = False reach_outside = False found = [] blocked_level = vertical + 1 blocked_right = horizontal + 1 blocked_left = horizontal + 1 blocked_down_right = horizontal + 1 blocked_down_left = horizontal + 1 # mark diagonally for dh in range(0, horizontal + 1): # check down as far as possible, Mario can fall down the whole level until he hits a solid block if blocked_down_right == horizontal + 1: blocked_down_right = check_blocked(level, i, j, dh, 0, right=True) if blocked_down_right >= dh: found_rechable, found_outside, positions = check_down( level, reachability_map, i, j, dh, check_outside, right=True ) if found_rechable: found_first = True if found_outside: reach_outside = True found.extend(positions) if blocked_down_left == horizontal + 1: blocked_down_left = check_blocked(level, i, j, dh, 0, right=False) if blocked_down_left >= dh: found_rechable, found_outside, positions = check_down( level, reachability_map, i, j, dh, check_outside, right=False ) if found_rechable: found_first = True if found_outside: reach_outside = True found.extend(positions) for dv in range(0, vertical + 1): if dh != 0 or dv != 0: if dv >= blocked_level: break # check if vertical path is blocked if dh == 0: if level[i - dv][j] != empty: blocked_level = dv continue # check if horizontal right path is blocked if blocked_right == horizontal + 1: blocked_right = check_blocked(level, i, j, dh, dv, right=True) if dh <= blocked_right and dh + dv <= 10: # right and up x = min(max(i - dv, 0), reachability_map.shape[0] - 1) right = j + dh if right > reachability_map.shape[1] - 1 and check_outside: reach_outside = True y = min(max(right, 0), reachability_map.shape[1] - 1) above = x - 1 if ( level[x][y] != empty and above >= 0 and level[above][y] == empty and reachability_map[x][y] != 1 ): reachability_map[x][y] = 1 found.append((x, y)) found_first = True # check if horizontal left path is blocked if blocked_left == horizontal + 1: blocked_left = check_blocked(level, i, j, dh, dv, right=False) if dh <= blocked_left and dh + dv <= 10: # left and up x = min(max(i - dv, 0), reachability_map.shape[0] - 1) y = min(max(j - dh, 0), reachability_map.shape[1] - 1) above = x - 1 if ( level[x][y] != empty and above >= 0 and level[above][y] == empty and reachability_map[x][y] != 1 ): reachability_map[x][y] = 1 found.append((x, y)) found_first = True return found_first, found, reach_outside
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CARL-main/carl/envs/mario/toad_gan.py
from typing import Optional import functools import os import sys from dataclasses import dataclass import torch from torch import Tensor from carl.envs.mario.generate_sample import generate_sample, generate_spatial_noise from carl.envs.mario.reachabillity import reachability_map @dataclass class TOADGAN: def __init__( self, Gs: Tensor, Zs: Tensor, reals: Tensor, NoiseAmp: Tensor, token_list: Tensor, num_layers: int, ): self.generators = Gs self.noise_maps = Zs self.reals = reals self.noise_amplitudes = NoiseAmp self.token_list = token_list self.num_layer = num_layers @property def original_height(self) -> int: return self.reals[-1].shape[-2] @property def original_width(self) -> int: return self.reals[-1].shape[-1] GENERATOR_DIR = os.path.abspath( os.path.join(os.path.dirname(__file__), "TOAD-GUI", "generators", "v2") ) GENERATOR_PATHS = sorted( os.listdir(GENERATOR_DIR), key=lambda name: [int(index) for index in name.replace("TOAD_GAN_", "").split("-")], ) @functools.lru_cache(maxsize=None) def load_generator(level_index: int) -> TOADGAN: import carl.envs.mario.models as models sys.modules["models"] = models gen_path = os.path.join(GENERATOR_DIR, GENERATOR_PATHS[level_index]) reals = torch.load( "%s/reals.pth" % gen_path, map_location="cuda:0" if torch.cuda.is_available() else "cpu", ) Zs = torch.load( "%s/noise_maps.pth" % gen_path, map_location="cuda:0" if torch.cuda.is_available() else "cpu", ) NoiseAmp = torch.load( "%s/noise_amplitudes.pth" % gen_path, map_location="cuda:0" if torch.cuda.is_available() else "cpu", ) token_list = torch.load("%s/token_list.pth" % gen_path) num_layers = torch.load("%s/num_layer.pth" % gen_path) Gs = torch.load( "%s/generators.pth" % gen_path, map_location="cuda:0" if torch.cuda.is_available() else "cpu", ) return TOADGAN( Gs=Gs, Zs=Zs, reals=reals, NoiseAmp=NoiseAmp, num_layers=num_layers, token_list=token_list, ) def generate_level( width: int, height: int, level_index: int, initial_noise: Optional[torch.Tensor] = None, filter_unplayable: bool = True, ) -> str: toad_gan = load_generator(level_index) playable = False level = None tries = 0 while not playable: tries += 1 level = generate_sample( **vars(toad_gan), scale_h=width / toad_gan.original_width, scale_v=height / toad_gan.original_height, initial_noise=initial_noise, ) if filter_unplayable and tries < 100: _, playable = reachability_map( level, shape=(height, width), check_outside=True ) else: playable = True assert level return "".join(level) def generate_initial_noise(width: int, height: int, level_index: int) -> Tensor: toad_gan = load_generator(level_index) base_noise_map = toad_gan.noise_maps[0] nzx = ( (base_noise_map.shape[2] - 2 * toad_gan.num_layer) * height / toad_gan.original_height ) nzy = ( (base_noise_map.shape[3] - 2 * toad_gan.num_layer) * width / toad_gan.original_width ) noise_shape = (1, len(toad_gan.token_list), int(round(nzx)), int(round(nzy))) noise = generate_spatial_noise(noise_shape) return noise
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CARL-main/carl/envs/mario/utils.py
from typing import Tuple import atexit import os import socket import sys from contextlib import closing from py4j.java_gateway import JavaGateway from xvfbwrapper import Xvfb MARIO_AI_PATH = os.path.abspath( os.path.join(os.path.dirname(__file__), "Mario-AI-Framework") ) _gateway = None _port = None def find_free_port() -> int: with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(("", 0)) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) return s.getsockname()[1] def load_level(level_name: str = "lvl-1.txt") -> str: prefix = ( os.path.join(MARIO_AI_PATH, "levels", "original") if level_name.startswith("lvl-") else "" ) with open(os.path.join(prefix, level_name), "r") as f: level = f.read() return level def get_port() -> int: global _gateway global _port if _gateway is None: _gateway, _port = launch_gateway() return _port def launch_gateway() -> Tuple[JavaGateway, int]: vdisplay = Xvfb(width=1280, height=740, colordepth=16) vdisplay.start() atexit.register(lambda: vdisplay.stop()) free_port = find_free_port() return ( JavaGateway.launch_gateway( classpath=os.path.join(MARIO_AI_PATH, "carl"), redirect_stderr=sys.stderr, redirect_stdout=sys.stdout, die_on_exit=True, port=free_port, ), free_port, )
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CARL-main/carl/envs/mario/models/__init__.py
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CARL-main/carl/envs/mario/models/conv_block.py
# Code from https://github.com/Mawiszus/TOAD-GAN from typing import Tuple, Union import torch.nn as nn class ConvBlock(nn.Sequential): """Conv block containing Conv2d, BatchNorm2d and LeakyReLU Layers.""" def __init__( self, in_channel: int, out_channel: int, ker_size: Union[int, Tuple[int, int]], padd: Union[str, Union[int, Tuple[int, int]]], stride: Union[int, Tuple[int, int]], ): super().__init__() self.add_module( "conv", nn.Conv2d( in_channel, out_channel, kernel_size=ker_size, stride=stride, padding=padd, ), ) self.add_module("norm", nn.BatchNorm2d(out_channel)) self.add_module("LeakyRelu", nn.LeakyReLU(0.2, inplace=True))
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CARL-main/carl/envs/mario/models/discriminator.py
# Code from https://github.com/Mawiszus/TOAD-GAN from argparse import Namespace import torch import torch.nn as nn from torch import Tensor from .conv_block import ConvBlock class Level_WDiscriminator(nn.Module): """Patch based Discriminator. Uses Namespace opt.""" def __init__(self, opt: Namespace): super().__init__() self.is_cuda = torch.cuda.is_available() N = int(opt.nfc) self.head = ConvBlock(opt.nc_current, N, (3, 3), opt.padd_size, 1) self.body = nn.Sequential() for i in range(opt.num_layer - 2): block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) self.body.add_module("block%d" % (i + 1), block) block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) self.body.add_module("block%d" % (opt.num_layer - 2), block) self.tail = nn.Conv2d(N, 1, kernel_size=(3, 3), stride=1, padding=opt.padd_size) def forward(self, x: Tensor) -> Tensor: x = self.head(x) x = self.body(x) x = self.tail(x) return x
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CARL-main/carl/envs/mario/models/generator.py
# Code from https://github.com/Mawiszus/TOAD-GAN from argparse import Namespace import torch import torch.nn as nn import torch.nn.functional as F from torch import Tensor from .conv_block import ConvBlock class Level_GeneratorConcatSkip2CleanAdd(nn.Module): """Patch based Generator. Uses namespace opt.""" def __init__(self, opt: Namespace): super().__init__() self.is_cuda = torch.cuda.is_available() N = int(opt.nfc) self.head = ConvBlock(opt.nc_current, N, (3, 3), opt.padd_size, 1) self.body = nn.Sequential() for i in range(opt.num_layer - 2): block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) self.body.add_module("block%d" % (i + 1), block) block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) self.body.add_module("block%d" % (opt.num_layer - 2), block) self.tail = nn.Sequential( nn.Conv2d( N, opt.nc_current, kernel_size=(3, 3), stride=1, padding=opt.padd_size ), ) def forward(self, x: Tensor, y: Tensor, temperature: float = 1) -> Tensor: x = self.head(x) x = self.body(x) x = self.tail(x) x = F.softmax( x * temperature, dim=1 ) # Softmax is added here to allow for the temperature parameter ind = int((y.shape[2] - x.shape[2]) / 2) y = y[:, :, ind : (y.shape[2] - ind), ind : (y.shape[3] - ind)] return x + y
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CARL-main/carl/envs/mario/sprites/README.md
## Notice This folder contains the sprite images from https://github.com/amidos2006/Mario-AI-Framework/tree/master/img. They are necessary for the Mario-AI-Framework and our level preview renderer.
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CARL-main/carl/envs/rna/__init__.py
# flake8: noqa: F401 # isort: skip_file try: from carl.envs.rna.carl_rna import CARLRnaDesignEnv from carl.envs.rna.carl_rna_definitions import ( DEFAULT_CONTEXT as CARLRnaDesignEnv_defaults, CONTEXT_BOUNDS as CARLRnaDesignEnv_bounds, ) except Exception as e: print(e)
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CARL-main/carl/envs/rna/carl_rna.py
# pylint: disable=missing-module-docstring # isort: skip_file from typing import Optional, Dict, Union, List, Tuple, Any import numpy as np import gym from carl.envs.carl_env import CARLEnv from carl.envs.rna.parse_dot_brackets import parse_dot_brackets from carl.envs.rna.rna_environment import ( RnaDesignEnvironment, RnaDesignEnvironmentConfig, ) from carl.utils.trial_logger import TrialLogger from carl.envs.rna.carl_rna_definitions import ( DEFAULT_CONTEXT, ACTION_SPACE, OBSERVATION_SPACE, CONTEXT_BOUNDS, ) from carl.utils.types import Context, Contexts from carl.context.selection import AbstractSelector class CARLRnaDesignEnv(CARLEnv): def __init__( self, env: gym.Env = None, data_location: str = "carl/envs/rna/learna/data", contexts: Contexts = {}, hide_context: bool = False, add_gaussian_noise_to_context: bool = False, gaussian_noise_std_percentage: float = 0.01, logger: Optional[TrialLogger] = None, scale_context_features: str = "no", default_context: Optional[Context] = DEFAULT_CONTEXT, max_episode_length: int = 500, state_context_features: Optional[List[str]] = None, context_mask: Optional[List[str]] = None, dict_observation_space: bool = False, context_selector: Optional[ Union[AbstractSelector, type[AbstractSelector]] ] = None, context_selector_kwargs: Optional[Dict] = None, obs_low: Optional[int] = 11, obs_high: Optional[int] = 11, ): """ Parameters ---------- env: gym.Env, optional Defaults to classic control environment mountain car from gym (MountainCarEnv). contexts: List[Dict], optional Different contexts / different environment parameter settings. instance_mode: str, optional """ if not contexts: contexts = {0: DEFAULT_CONTEXT} if env is None: env_config = RnaDesignEnvironmentConfig( mutation_threshold=DEFAULT_CONTEXT["mutation_threshold"], reward_exponent=DEFAULT_CONTEXT["reward_exponent"], state_radius=DEFAULT_CONTEXT["state_radius"], ) dot_brackets = parse_dot_brackets( dataset=DEFAULT_CONTEXT["dataset"], # type: ignore[arg-type] data_dir=data_location, target_structure_ids=DEFAULT_CONTEXT["target_structure_ids"], # type: ignore[arg-type] ) env = RnaDesignEnvironment(dot_brackets, env_config) env.action_space = ACTION_SPACE env.observation_space = OBSERVATION_SPACE env.reward_range = (-np.inf, np.inf) env.metadata = {} # The data_location in the RNA env refers to the place where the dataset is downloaded to, so it is not changed # with the context. env.data_location = data_location super().__init__( env=env, contexts=contexts, hide_context=hide_context, add_gaussian_noise_to_context=add_gaussian_noise_to_context, gaussian_noise_std_percentage=gaussian_noise_std_percentage, logger=logger, scale_context_features=scale_context_features, default_context=default_context, max_episode_length=max_episode_length, state_context_features=state_context_features, dict_observation_space=dict_observation_space, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, context_mask=context_mask, ) self.whitelist_gaussian_noise = list(DEFAULT_CONTEXT) self.obs_low = obs_low self.obs_high = obs_high def step(self, action: np.ndarray) -> Tuple[List[int], float, Any, Any]: # Step function has a different name in this env state, reward, done = self.env.execute(action) # type: ignore[has-type] self.step_counter += 1 return state, reward, done, {} def _update_context(self) -> None: dot_brackets = parse_dot_brackets( dataset=self.context["dataset"], data_dir=self.env.data_location, # type: ignore[has-type] target_structure_ids=self.context["target_structure_ids"], ) env_config = RnaDesignEnvironmentConfig( mutation_threshold=self.context["mutation_threshold"], reward_exponent=self.context["reward_exponent"], state_radius=self.context["state_radius"], ) self.env = RnaDesignEnvironment(dot_brackets, env_config) self.build_observation_space( env_lower_bounds=-np.inf * np.ones(self.obs_low), env_upper_bounds=np.inf * np.ones(self.obs_high), context_bounds=CONTEXT_BOUNDS, # type: ignore[arg-type] )
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CARL-main/carl/envs/rna/carl_rna_definitions.py
import numpy as np from gym import spaces DEFAULT_CONTEXT = { "mutation_threshold": 5, "reward_exponent": 1, "state_radius": 5, "dataset": "eterna", "target_structure_ids": None, } CONTEXT_BOUNDS = { "mutation_threshold": (0.1, np.inf, float), "reward_exponent": (0.1, np.inf, float), "state_radius": (1, np.inf, float), "dataset": ("eterna", "rfam_taneda", None), "target_structure_ids": ( 0, np.inf, [list, int], ), # This is conditional on the dataset (and also a list) } ACTION_SPACE = spaces.Discrete(4) OBSERVATION_SPACE = spaces.Box(low=-np.inf * np.ones(11), high=np.inf * np.ones(11))
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CARL-main/carl/envs/rna/parse_dot_brackets.py
# flake8: noqa: F401 # isort: skip_file from pathlib import Path from typing import List, Optional, Union, Generator def parse_dot_brackets( dataset: str, data_dir: str, target_structure_ids: Optional[List[int]] = None, target_structure_path: Optional[Union[str, Path]] = None, ) -> List[str]: """Generate the targets for next epoch. Parameters ---------- dataset : str The name of the benchmark to use targets from data_dir : str The directory of the target structures. target_structure_ids : Optional[Union[List[int], int]], optional Use specific targets by ids., by default None target_structure_path : Optional[str], optional pecify a path to the targets., by default None Returns ------- Generator[int] An epoch generator for the specified target structures. """ if target_structure_path: target_paths = [target_structure_path] elif target_structure_ids: target_paths = [ Path(data_dir, dataset, f"{id_}.rna") for id_ in target_structure_ids ] else: target_paths = list(Path(data_dir, dataset).glob("*.rna")) return [data_path.read_text().rstrip() for data_path in target_paths] # type: ignore[union-attr]
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CARL-main/carl/envs/rna/readme.md
# **CARL RNA Environment** This is the CARL RNA environment that has been adapted from the [Learning to Design RNA](https://openreview.net/pdf?id=ByfyHh05tQ) by Runge et. al. The code has been adapted from [https://github.com/automl/learna](https://github.com/automl/learna) with a carl wrapper written around the envionment. ## Datasets To download and build the datasets we report on in our publications, namely the Rfam-Taneda [[Taneda, 2011]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169953/pdf/aabc-4-001.pdf) dataset and our three proposed datasets, Rfam-Learn-Train, Rfam-Learn-Validation and Rfam-Learn-Test, run the following command after installation of all requirements. ``` cd data ./download_and_build_rfam_learn.sh ./download_and_build_rfam_taneda.sh ``` This will download all files and save them into the `data/` directory.
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CARL-main/carl/envs/rna/rna_environment.py
# isort: skip_file """ Code adapted from https://github.com/automl/learna """ import time from itertools import product from dataclasses import dataclass from distance import hamming import numpy as np from RNA import fold import gym from typing import Any, List @dataclass class RnaDesignEnvironmentConfig: """ Dataclass for the configuration of the environment. Parameters ---------- mutation_threshold: Defines the minimum distance needed before applying the local improvement step. reward_exponent: A parameter to shape the reward function. state_radius: The state representation is a (2*<state_radius> + 1)-gram at each position. use_conv: Bool to state if a convolutional network is used or not. use_embedding: Bool to state if embedding is used or not. """ mutation_threshold: Any = 5 reward_exponent: Any = 1.0 state_radius: Any = 5 use_conv: bool = True use_embedding: bool = False def _string_difference_indices(s1, s2): # type: ignore[no-untyped-def] """ Returns all indices where s1 and s2 differ. Parameters ---------- s1: The first sequence. s2: The second sequence. Returns ------- List of indices where s1 and s2 differ. """ return [index for index in range(len(s1)) if s1[index] != s2[index]] def _encode_dot_bracket( # type: ignore[no-untyped-def] secondary: str, env_config: RnaDesignEnvironmentConfig ): # type: ignore[no-untyped-def] """ Encode the dot_bracket notated target structure. The encoding can either be binary or by the embedding layer. Parameters ---------- secondary: The target structure in dot_bracket notation. env_config: The configuration of the environment. Returns ------- List of encoding for each site of the padded target structure. """ padding = "=" * env_config.state_radius padded_secondary = padding + secondary + padding if env_config.use_embedding: site_encoding = {".": 0, "(": 1, ")": 2, "=": 3} else: site_encoding = {".": 0, "(": 1, ")": 1, "=": 0} # Sites corresponds to 1 pixel with 1 channel if convs are applied directly if env_config.use_conv and not env_config.use_embedding: return [[site_encoding[site]] for site in padded_secondary] return [site_encoding[site] for site in padded_secondary] def _encode_pairing(secondary: str): # type: ignore[no-untyped-def] pairing_encoding = [None] * len(secondary) stack = [] for index, symbol in enumerate(secondary, 0): if symbol == "(": stack.append(index) elif symbol == ")": paired_site = stack.pop() pairing_encoding[paired_site] = index # type: ignore[no-untyped-def,call-overload] pairing_encoding[index] = paired_site # type: ignore[call-overload] return pairing_encoding class _Target(object): """ Class of the target structure. Provides encodings and id. """ _id_counter = 0 def __init__(self, dot_bracket, env_config): # type: ignore[no-untyped-def] """ Initialize a target structure. Parameters ---------- dot_bracket: dot_bracket encoded target structure. env_config: The environment configuration. """ _Target._id_counter += 1 self.id = _Target._id_counter # For processing results self.dot_bracket = dot_bracket self._pairing_encoding = _encode_pairing(self.dot_bracket) self.padded_encoding = _encode_dot_bracket(self.dot_bracket, env_config) def __len__(self): # type: ignore[no-untyped-def] return len(self.dot_bracket) def get_paired_site(self, site): # type: ignore[no-untyped-def] """ Get the paired site for <site> (base pair). Args: site: The site to check the pairing site for. Returns: The site that pairs with <site> if exists. """ return self._pairing_encoding[site] class _Design(object): """ Class of the designed candidate solution. """ action_to_base = {0: "G", 1: "A", 2: "U", 3: "C"} action_to_pair = {0: "GC", 1: "CG", 2: "AU", 3: "UA"} def __init__(self, length=None, primary=None): # type: ignore[no-untyped-def] """ Initialize a candidate solution. Parameters ---------- length: The length of the candidate solution. primary: The sequence of the candidate solution. """ if primary: self._primary_list = primary else: self._primary_list = [None] * length self._dot_bracket = None self._current_site = 0 def get_mutated(self, mutations, sites): # type: ignore[no-untyped-def] """ Locally change the candidate solution. Parameters ---------- mutations: Possible mutations for the specified sites sites: The sites to be mutated Returns ------- A Design object with the mutated candidate solution. """ mutatedprimary = self._primary_list.copy() for site, mutation in zip(sites, mutations): mutatedprimary[site] = mutation return _Design(primary=mutatedprimary) def assign_sites( # type: ignore[no-untyped-def] self, action, site, paired_site=None ): # type: ignore[no-untyped-def] """ Assign nucleotides to sites for designing a candidate solution. Parameters ---------- action: The agents action to assign a nucleotide. site: The site to which the nucleotide is assigned to. paired_site: Defines if the site is assigned with a base pair or not. """ self._current_site += 1 if paired_site: base_current, base_paired = self.action_to_pair[action] self._primary_list[site] = base_current self._primary_list[paired_site] = base_paired else: self._primary_list[site] = self.action_to_base[action] @property def first_unassigned_site(self): # type: ignore[no-untyped-def] try: while self._primary_list[self._current_site] is not None: self._current_site += 1 return self._current_site except IndexError: return None @property def primary(self): # type: ignore[no-untyped-def] return "".join(self._primary_list) def _random_epoch_gen(data): # type: ignore[no-untyped-def] """ Generator to get epoch data. Parameters ---------- data: The targets of the epoch """ while True: for i in np.random.permutation(len(data)): yield data[i] @dataclass class EpisodeInfo: """ Information class. """ __slots__ = ["target_id", "time", "normalized_hamming_distance"] target_id: int time: float normalized_hamming_distance: float class RnaDesignEnvironment(gym.Env): """ The environment for RNA design using deep reinforcement learning. """ def __init__( # type: ignore[no-untyped-def] self, dot_brackets: List[str], env_config ): # type: ignore[no-untyped-def] """Initialize the environment Args dot_brackets : dot_bracket encoded target structure env_config : The environment configuration. """ self._env_config = env_config targets = [ _Target(dot_bracket, self._env_config) for dot_bracket in dot_brackets ] self._target_gen = _random_epoch_gen(targets) self.target = None self.design = None self.episodes_info = [] # type: ignore[var-annotated] def __str__(self): # type: ignore[no-untyped-def] return "RnaDesignEnvironment" def seed(self, seed): # type: ignore[no-untyped-def] return None def reset(self): # type: ignore[no-untyped-def] """ Reset the environment. First function called by runner. Returns first state. Returns: The first state. """ self.target = next(self._target_gen) self.design = _Design(len(self.target)) return self._get_state() def _apply_action(self, action): # type: ignore[no-untyped-def] """ Assign a nucleotide to a site. Args: action: The action chosen by the agent. """ current_site = self.design.first_unassigned_site paired_site = self.target.get_paired_site( current_site ) # None for unpaired sites self.design.assign_sites(action, current_site, paired_site) def _get_state(self): # type: ignore[no-untyped-def] """ Get a state dependend on the padded encoding of the target structure. Returns: The next state. """ start = self.design.first_unassigned_site return self.target.padded_encoding[ start : start + 2 * self._env_config.state_radius + 1 ] def _local_improvement(self, folded_design): # type: ignore[no-untyped-def] """ Compute Hamming distance of locally improved candidate solutions. Agrs folded_design: The folded candidate solution. Returns: The minimum Hamming distance of all imporved candidate solutions. """ differing_sites = _string_difference_indices( self.target.dot_bracket, folded_design ) hamming_distances = [] for mutation in product("AGCU", repeat=len(differing_sites)): mutated = self.design.get_mutated(mutation, differing_sites) folded_mutated, _ = fold(mutated.primary) hamming_distance = hamming(folded_mutated, self.target.dot_bracket) hamming_distances.append(hamming_distance) if hamming_distance == 0: # For better timing results return 0 return min(hamming_distances) def _get_reward(self, terminal): # type: ignore[no-untyped-def] """ Compute the reward after assignment of all nucleotides. Args: terminal: Bool defining if final timestep is reached yet. Returns: The reward at the terminal timestep or 0 if not at the terminal timestep. """ if not terminal: return 0 folded_design, _ = fold(self.design.primary) hamming_distance = hamming(folded_design, self.target.dot_bracket) if 0 < hamming_distance < self._env_config.mutation_threshold: hamming_distance = self._local_improvement(folded_design) normalized_hamming_distance = hamming_distance / len(self.target) # For hparam optimization episode_info = EpisodeInfo( target_id=self.target.id, time=time.time(), normalized_hamming_distance=normalized_hamming_distance, ) self.episodes_info.append(episode_info) return (1 - normalized_hamming_distance) ** self._env_config.reward_exponent def execute(self, actions): # type: ignore[no-untyped-def] """ Execute one interaction of the environment with the agent. Args: action: Current action of the agent. Returns: state: The next state for the agent. terminal: The signal for end of an episode. reward: The reward if at terminal timestep, else 0. """ self._apply_action(actions) terminal = self.design.first_unassigned_site is None state = None if terminal else self._get_state() reward = self._get_reward(terminal) return state, terminal, reward def close(self): # type: ignore[no-untyped-def] pass @property def states(self): # type: ignore[no-untyped-def] type = "int" if self._env_config.use_embedding else "float" if self._env_config.use_conv and not self._env_config.use_embedding: return dict(type=type, shape=(1 + 2 * self._env_config.state_radius, 1)) return dict(type=type, shape=(1 + 2 * self._env_config.state_radius,)) @property def actions(self): # type: ignore[no-untyped-def] return dict(type="int", num_actions=4)
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CARL-main/carl/envs/rna/data/download_and_build_eterna.py
# flake8: noqa: F401 # # isort: skip_file from urllib.request import Request from tqdm import tqdm import requests # type: ignore[import] def _download_dataset_from_http(url: str, download_path: str) -> None: """Donwload the dataset from a given url Parameters ---------- url : string URL for the dataset location download_path : str Location of storing the dataset """ response = requests.get(url, stream=True) with open(download_path, "wb+") as dataset_file: progress_bar = tqdm( unit="B", unit_scale=True, unit_divisor=1024, total=int(response.headers["Content-Length"]), ) for data in tqdm(response.iter_content()): progress_bar.update(len(data)) dataset_file.write(data) def download_eterna(download_path: str) -> None: eterna_url = ( "https://ars.els-cdn.com/content/image/1-s2.0-S0022283615006567-mmc5.txt" ) _download_dataset_from_http(eterna_url, download_path) def extract_secondarys(download_path: str, dump_path: str) -> None: """Download secondary information/features Parameters ---------- download_path : str path to downloaded files dump_path : str path to dump secondary features """ with open(download_path) as input: parsed = list(zip(*(line.strip().split("\t") for line in input))) secondarys = parsed[4][1:] with open(dump_path, "w") as data_file: for structure in secondarys: if structure[-1] == "_": # Weird dataset bug structure = structure[:-1] data_file.write(f"{structure}\n") if __name__ == "__main__": download_path = f'{"data/eterna/raw/eterna_raw.txt"}' dump_path = f'{"data/eterna/interim/eterna.txt"}' download_eterna(download_path) extract_secondarys(download_path, dump_path)
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CARL-main/carl/envs/rna/data/download_and_build_rfam_learn.sh
#!/usr/bin/env bash # So far RNA has been tested only on linux systems mkdir -p data/rfam_learn/{raw,test,train,validation} cd data/ wget https://www.dropbox.com/s/cfhnkzdx4ciy7zf/rfam_learn.tar.gz?dl=1 -O rfam_learn.tar.gz tar xf rfam_learn.tar.gz rm -f rfam_learn.tar.gz
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CARL-main/carl/envs/rna/data/download_and_build_rfam_taneda.sh
# So far RNA has been tested only on linux systems cd data/ mkdir rfam_taneda cd rfam_taneda wget rna.eit.hirosaki-u.ac.jp/modena/v0028/linux/modena.dataset.tar.gz tar -xf modena.dataset.tar.gz rm -f modena.dataset.tar.gz rm -rf ct_version i=1 while [[ i -le 30 ]]; do if [ $i == 23 ]; then : elif [[ $i -le 9 ]]; then cat RF0000$i* > $i.rna; elif [[ $i -le 22 ]]; then cat RF000$i* > $i.rna; else cat RF000$i* > $(($i - 1)).rna; fi let i=$i+1; done rm -f *.ss
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CARL-main/carl/envs/rna/data/parse_dot_brackets.py
# flake8: noqa: F401 # isort: skip_file from email.generator import Generator from pathlib import Path from typing import List def parse_dot_brackets( dataset: str, data_dir: str, target_structure_ids: List[int] = None, target_structure_path: Path = None, ) -> List[str]: """Generate the targets for next epoch. The most common encoding for the RNA secondary structure is the dot-bracket notation, consisting in a balanced parentheses string composed by a three-character alphabet {.,(,)}, that can be unambiguously converted in the RNA secondary structure. Parameters ---------- dataset: str The name of the benchmark to use targets from data_dir: str The directory of the target structures. target_structure_ids: List[int] Use specific targets by ids. target_structure_path: str Specify a path to the targets Returns ------- List[str] An epoch generator for the specified target structure(s) """ if target_structure_path: target_paths = [target_structure_path] elif target_structure_ids: target_paths = [ Path(data_dir, dataset, f"{id_}.rna") for id_ in target_structure_ids ] else: target_paths = list(Path(data_dir, dataset).glob("*.rna")) return [data_path.read_text().rstrip() for data_path in target_paths]
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CARL-main/carl/envs/rna/data/secondaries_to_single_files.sh
# So far RNA has been tested only on linux systems DATAPATH=$1 SECONDARY_FILE=$2 NUM_SECONDARIES=$(cat $SECONDARY_FILE | wc -l ) i=1; while [[ i -le $NUM_SECONDARIES ]]; do awk "NR==$i{print;exit}" $SECONDARY_FILE > $DATAPATH/$i.rna; let i=$i+1; done Footer
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CARL-main/carl/utils/__init__.py
0
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CARL-main/carl/utils/trial_logger.py
from typing import Union import argparse from pathlib import Path import configargparse import pandas as pd from carl.utils.types import Context class TrialLogger(object): """ Holds all train arguments and sets up logging directory and stables baselines logging, writes trial setup and writes context feature history. Following logging happens at the corresponding events: after each step: reward (progress.csv) (StableBaselines logger) step (progress.csv) (StableBaselines logger) after each episode: context (context_history.csv) (TrialLogger) episode (context_history.csv) (TrialLogger) step (context_history.csv) (TrialLogger) once on train start: experiment config (env, agent, seed, set of contexts) (TrialLogger) hyperparameters """ def __init__( self, logdir: Union[str, Path], parser: configargparse.ArgParser, trial_setup_args: argparse.Namespace, add_context_feature_names_to_logdir: bool = False, ): """ Parameters ---------- logdir: Union[str, Path] Base logging directory. The actual logging directory, accessible via self.logdir, is logdir / "{agent}_{seed}". Agent and seed are provided via trial_setup_args. If add_context_feature_names_to_logdir is True, the logging directory will be logdir / context_feature_dirname /f"{agent}_{seed}". context_feature_dirname are all context feature names provided via trial_setup_args.context_feature_args joined by "__". parser: configargparse.ArgParser Argument parser containing all arguments from runscript. Needed to write trial setup file. trial_setup_args: argparse.Namespace Parsed arguments from parser. Arguments are supposed to be parsed before in case new arguments are added via some external logic. add_context_feature_names_to_logdir: bool, False See logdir for effect. """ self.parser = parser seed = trial_setup_args.seed agent = trial_setup_args.agent if add_context_feature_names_to_logdir: context_feature_args = trial_setup_args.context_feature_args names = [ n for n in context_feature_args if "std" not in n and "mean" not in n ] # TODO make sure to exclude numbers context_feature_dirname = "default" if names: context_feature_dirname = ( names[0] if len(names) == 1 else "__".join(names) ) self.logdir = Path(logdir) / context_feature_dirname / f"{agent}_{seed}" else: self.logdir = Path(logdir) / f"{agent}_{seed}" self.logdir.mkdir(parents=True, exist_ok=True) self.trial_setup_args = trial_setup_args self.trial_setup_fn = self.logdir / "trial_setup.ini" self.context_history_fn = self.logdir / "context_history.csv" self.prepared_context_history_file = False def write_trial_setup(self) -> None: """ Write trial setup to file with path logdir / "trial_setup.ini". Returns ------- None """ output_file_paths = [str(self.trial_setup_fn)] self.parser.write_config_file( parsed_namespace=self.trial_setup_args, output_file_paths=output_file_paths ) def write_context(self, episode: int, step: int, context: Context) -> None: """ Context will be written to csv file (logdir / "context_history.csv"). The format is as follows: episode,step,context_feature_0,context_feature_1,...,context_feature_n 0,1,345345,234234,...,234234 Parameters ---------- episode: int Episode. step: int Timestep. context: Context Keys: Context features names/ids, values: context feature values. Returns ------- None """ columns = ["episode", "step"] + list(context.keys()) values = [episode, step] + list(context.values()) df = pd.DataFrame(values).T df.columns = columns write_header = False mode = "a" if not self.prepared_context_history_file: write_header = True mode = "w" self.prepared_context_history_file = True df.to_csv( path_or_buf=self.context_history_fn, sep=",", header=write_header, index=False, mode=mode, )
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CARL-main/carl/utils/types.py
from typing import Any, Dict, List, TypeVar, Union import numpy as np Context = Dict[str, Any] Contexts = Dict[Any, Context] Vector = Union[List[Any], np.ndarray] ObsType = TypeVar("ObsType")
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CARL-main/carl/utils/doc_building/__init__.py
0
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CARL-main/carl/utils/doc_building/build.py
from typing import Dict, Tuple from pathlib import Path import numpy as np import pandas as pd from gym import spaces import carl.envs # JUST FOR DOCS / is dynamic in code!! MARIO_OBSERVATION_SPACE = spaces.Box( low=0, high=255, shape=[4, 64, 64], dtype=np.uint8, ) # is dynamic in code MARIO_ACTION_SPACE = spaces.Discrete(n=10) def build() -> Tuple[pd.DataFrame, Dict, Dict]: filepath = Path(__file__) outdir = filepath.parent.parent.parent.parent / "docs/source/environments/data" print("Build environment overview table.") # Create snapshot local_vars = vars(carl.envs) k_env_family = "Env. Family" k_env_name = "Name" k_n_context_features = "# Context Features" k_action_space = "Action Space" k_obs_space = "Obs. Space" # Filter envs mustinclude = "CARL" forbidden = ["defaults", "bounds"] overview_table_entries = [] bounds_entries = {} defaults_entries = {} for varname, var in local_vars.items(): if mustinclude in varname and not np.any([f in varname for f in forbidden]): env_name = varname module = var.__module__ env_family = module.split(".")[-2] env = var() action_space = str(env.env.action_space) observation_space = str(env.env.observation_space) context = env.contexts[list(env.contexts.keys())[0]] n_context_features = len(context) data = { k_env_family: env_family, k_env_name: varname, k_n_context_features: n_context_features, k_action_space: action_space, k_obs_space: observation_space, } overview_table_entries.append(data) defaults_entries[env_name] = local_vars[f"{env_name}_defaults"] bounds_entries[env_name] = local_vars[f"{env_name}_bounds"] # if len(overview_table_entries) == 3: # TODO change back # break # Add Mario Information env_families = ["Mario"] env_names = ["CARLMarioEnv"] from carl.envs.mario.carl_mario_definitions import CONTEXT_BOUNDS as mario_bounds from carl.envs.mario.carl_mario_definitions import DEFAULT_CONTEXT as mario_defaults unicorn_defaults = [mario_defaults] N_context_features = [len(c) for c in unicorn_defaults] action_spaces = [MARIO_ACTION_SPACE] observation_spaces = [MARIO_OBSERVATION_SPACE] unicorn_bounds = [mario_bounds] for i in range(len(env_names)): data = { k_env_family: env_families[i], k_env_name: env_names[i], k_n_context_features: N_context_features[i], k_action_space: action_spaces[i], k_obs_space: observation_spaces[i], } overview_table_entries.append(data) defaults_entries[env_names[i]] = unicorn_defaults[i] bounds_entries[env_names[i]] = unicorn_bounds[i] df = pd.DataFrame(overview_table_entries) # Save overview table csv_filename = outdir / "tab_overview_environments.csv" csv_filename.parent.mkdir(exist_ok=True, parents=True) overview_columns = [ k_env_family, k_env_name, k_n_context_features, k_action_space, k_obs_space, ] save_df = df[overview_columns] save_df.to_csv(csv_filename, index=False) env_names = list(defaults_entries.keys()) for env_name in env_names: fname = outdir / f"context_definitions/{env_name}.csv" fname.parent.mkdir(parents=True, exist_ok=True) defaults = defaults_entries[env_name] defaults_df = pd.Series(defaults) defaults_df.index.name = "Context Feature" defaults_df.name = "Default" bounds = bounds_entries[env_name] bounds_df = pd.Series(bounds) bounds_df.index.name = "Context Feature" bounds_df.name = "Bounds" context_def_df = pd.concat([defaults_df, bounds_df], axis=1) context_def_df.to_csv(fname) print("Done!") return df, defaults_entries, bounds_entries if __name__ == "__main__": df, defaults_entries, bounds_entries = build()
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CARL-main/carl/utils/doc_building/plot_context_space.py
""" Boxplot - number of context features - percentage of continuous CFs - number of CFs changing the dynamics - number of CFs changing the reward """ from __future__ import annotations if __name__ == "__main__": from pathlib import Path import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import carl.envs global_vars = vars(carl.envs) vars = { k: v for k, v in global_vars.items() if "Env" in k or "Meta" in k or "CARL" in k } env_names = [n for n in vars.keys() if "bounds" not in n and "defaults" not in n] env_context_feature_names = {} context_feature_names: list[str] = [] dfs: list[pd.DataFrame] = [] n_context_features_per_env = [] n_float_cfs = 0 for env_name in env_names: defaults = pd.Series(vars[env_name + "_defaults"]) n_context_features_per_env.append(len(defaults)) bounds = vars[env_name + "_bounds"] bounds_vals = list(bounds.values()) n_float_cfs += np.sum([1 for v in bounds_vals if v[2] == float]) env_context_feature_names[env_name] = defaults.keys() n_context_features = np.sum(n_context_features_per_env) n_reward_changing = 7 n_dynami_changing = 129 env_names.extend(["CARLMarioEnv", "CARLRnaDesignEnv"]) n_context_features += 3 + 5 n_float_cfs += 0 + 0 # integers == continuous? percentage_float_cfs = n_float_cfs / n_context_features dfp = pd.Series( { "$n_{{total}}$": n_context_features, "$n_{{dynamics}}$": n_dynami_changing, "$n_{{reward}}$": n_reward_changing, "$n_{{continuous}}$": n_float_cfs, } ) dfp.name = "Context Features" vals = dfp.to_numpy() labels = dfp.index fontsize = 15 sns.set_style("whitegrid") figsize = (2.5, 2) dpi = 200 fname = "plots/context_feature_statistics.png" p = Path(fname) p.parent.mkdir(exist_ok=True, parents=True) fig = plt.figure(figsize=figsize, dpi=dpi) ax = fig.add_subplot(111) ax = sns.barplot(x=vals, y=labels, ax=ax, palette="colorblind", orient="h") xmin = 0 xmax = max(vals) ax.set_xlim(xmin, xmax) ax.set_frame_on(False) ax.axes.get_yaxis().set_visible(False) for i, label in enumerate(labels): x = 10 y = i + 0.2 text = f"{label} = {vals[i]:.0f}" ax.text(x, y, text, fontsize=fontsize) fig.set_tight_layout(True) plt.show() fig.savefig(fname, bbox_inches="tight") dfp.to_csv(Path(fname).parent / "context_feature_statistics.csv")
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CARL-main/carl/utils/doc_building/plot_radar.py
from __future__ import annotations if __name__ == "__main__": from pathlib import Path import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from carl.utils.doc_building.plotting import radar_factory env_context_feature_names = { "CARLMountainCarEnv": [ "force", "goal_position", "goal_velocity", "gravity", "max_position", "max_speed", "min_position", "start_position", "start_position_std", "start_velocity", "start_velocity_std", ], "CARLPendulumEnv": ["dt", "g", "l", "m", "max_speed"], "CARLAcrobotEnv": [ "link_com_1", "link_com_2", "link_length_1", "link_length_2", "link_mass_1", "link_mass_2", "link_moi", "max_velocity_1", "max_velocity_2", ], "CARLCartPoleEnv": [ "force_magnifier", "gravity", "masscart", "masspole", "pole_length", "update_interval", ], "CARLMountainCarContinuousEnv": [ "goal_position", "goal_velocity", "max_position", "max_position_start", "max_speed", "max_velocity_start", "min_position", "min_position_start", "min_velocity_start", "power", ], "CARLLunarLanderEnv": [ "FPS", "GRAVITY_X", "GRAVITY_Y", "INITIAL_RANDOM", "LEG_AWAY", "LEG_DOWN", "LEG_H", "LEG_SPRING_TORQUE", "LEG_W", "MAIN_ENGINE_POWER", "SCALE", "SIDE_ENGINE_AWAY", "SIDE_ENGINE_HEIGHT", "SIDE_ENGINE_POWER", "VIEWPORT_H", "VIEWPORT_W", ], "CARLVehicleRacingEnv": ["VEHICLE"], "CARLBipedalWalkerEnv": [ "FPS", "FRICTION", "GRAVITY_X", "GRAVITY_Y", "INITIAL_RANDOM", "LEG_DOWN", "LEG_H", "LEG_W", "LIDAR_RANGE", "MOTORS_TORQUE", "SCALE", "SPEED_HIP", "SPEED_KNEE", "TERRAIN_GRASS", "TERRAIN_HEIGHT", "TERRAIN_LENGTH", "TERRAIN_STARTPAD", "TERRAIN_STEP", "VIEWPORT_H", "VIEWPORT_W", ], "CARLAnt": [ "actuator_strength", "angular_damping", "friction", "gravity", "joint_angular_damping", "joint_stiffness", "torso_mass", ], "CARLHalfcheetah": [ "angular_damping", "friction", "gravity", "joint_angular_damping", "joint_stiffness", "torso_mass", ], "CARLHumanoid": [ "angular_damping", "friction", "gravity", "joint_angular_damping", "torso_mass", ], "CARLFetch": [ "actuator_strength", "angular_damping", "friction", "gravity", "joint_angular_damping", "joint_stiffness", "target_distance", "target_radius", "torso_mass", ], "CARLGrasp": [ "actuator_strength", "angular_damping", "friction", "gravity", "joint_angular_damping", "joint_stiffness", "target_distance", "target_height", "target_radius", ], "CARLUr5e": [ "actuator_strength", "angular_damping", "friction", "gravity", "joint_angular_damping", "joint_stiffness", "target_distance", "target_radius", "torso_mass", ], "CARLRnaDesignEnv": [ "mutation_threshold", "reward_exponent", "state_radius", "dataset", "target_structure_ids", ], "CARLMarioEnv": ["level_index", "noise", "mario_state"], "CARLDmcWalkerEnv": [ "gravity", "friction_tangential", "friction_torsional", "friction_rolling", "timestep", "joint_damping", "joint_stiffness", "actuator_strength", "density", "viscosity", "geom_density", "wind_x", "wind_y", "wind_z", ], "CARLDmcQuadrupedEnv": [ "gravity", "friction_tangential", "friction_torsional", "friction_rolling", "timestep", "joint_damping", "joint_stiffness", "actuator_strength", "density", "viscosity", "geom_density", "wind_x", "wind_y", "wind_z", ], "CARLDmcFishEnv": [ "gravity", "friction_tangential", "friction_torsional", "friction_rolling", "timestep", "joint_damping", "joint_stiffness", "actuator_strength", "density", "viscosity", "geom_density", "wind_x", "wind_y", "wind_z", ], "CARLDmcFingerEnv": [ "gravity", "friction_tangential", "friction_torsional", "friction_rolling", "timestep", "joint_damping", "joint_stiffness", "actuator_strength", "density", "viscosity", "geom_density", "wind_x", "wind_y", "wind_z", "limb_length_0", "limb_length_1", "spinner_radius", "spinner_length", ], } action_space_sizes = [ (3,), (1,), (3,), (2,), (1,), (4,), (3,), (4,), (8,), (6,), (17,), (10,), (19,), (6,), (8,), (10,), (6,), (12,), (5,), (2,), ] state_space_sizes = [ (2,), (3,), (6,), (4,), (2,), (8,), (96, 96, 3), (24,), (87,), (23,), (299,), (101,), (132,), (66,), (11,), (64, 64, 3), (24,), (78,), (24,), (9,), ] n_context_features = [ 11, 5, 9, 6, 10, 16, 1, 20, 7, 6, 5, 9, 9, 9, 5, 3, 14, ] env_names = [ "CARLMountainCarEnv", "CARLPendulumEnv", "CARLAcrobotEnv", "CARLCartPoleEnv", "CARLMountainCarContinuousEnv", "CARLLunarLanderEnv", "CARLVehicleRacingEnv", "CARLBipedalWalkerEnv", "CARLAnt", "CARLHalfcheetah", "CARLHumanoid", "CARLFetch", "CARLGrasp", "CARLUr5e", "CARLRnaDesignEnv", "CARLMarioEnv", "CARLDmcWalkerEnv", "CARLDmcQuadrupedEnv", "CARLDmcFishEnv", "CARLDmcFingerEnv", ] n_cfs_d = [11, 5, 8, 6, 10, 16, 1, 20, 7, 6, 5, 9, 9, 9, 4, 3, 14, 14, 14, 18] n_cfs_r = [0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] n_cfs = 131 n_dynami_changing = 129 n_reward_changing = 7 n_float_cfs = 114 percentage_float_cfs = n_float_cfs / n_cfs env_types = { "classic_control": [ "CARLAcrobotEnv", "CARLCartPoleEnv", "CARLMountainCarEnv", "CARLMountainCarContinuousEnv", "CARLPendulumEnv", ], "box2d": ["CARLBipedalWalkerEnv", "CARLLunarLanderEnv", "CARLVehicleRacingEnv"], "brax": ["CARLAnt", "CARLFetch", "CARLGrasp", "CARLHumanoid", "CARLUr5e"], "dmc": [ "CARLDmcWalkerEnv", "CARLDmcQuadrupedEnv", "CARLDmcFishEnv", "CARLDmcFingerEnv", ], "misc": ["CARLMarioEnv", "CARLRnaDesignEnv"], } data: list[pd.DataFrame] = [] for env_type in env_types: envs = env_types[env_type] title = env_type ids = [env_names.index(e) for e in envs] # ss_sizes = [state_space_sizes[i][0] for i in ids] # as_sizes = [action_space_sizes[i][0] for i in ids] ss_sizes = [np.prod(state_space_sizes[i]) for i in ids] as_sizes = [np.prod(action_space_sizes[i]) for i in ids] reward_changing = [n_cfs_r[i] for i in ids] dynamics_changing = [n_cfs_d[i] for i in ids] cf_numbers = [len(env_context_feature_names[env_names[i]]) for i in ids] # print(ss_sizes, as_sizes, cf_numbers) data.append( pd.DataFrame( { "env_type": [env_type] * len(ids), "env_name": envs, "state_space_size": ss_sizes, "action_space_size": as_sizes, "n_context_features": cf_numbers, "n_cf_reward": reward_changing, "n_cf_dyna": dynamics_changing, } ) ) data = pd.concat(data) # normalize values cols = [c for c in data.columns if c not in ["env_type", "env_name"]] # type: ignore [attr-defined] max_values_per_col = [] for col in cols: if col == "state_space_size": data[col] = np.log(data[col]) max_val = data[col].max() max_values_per_col.append(max_val) data[col] /= max_val cols_plot = [ "state_space_size", "action_space_size", "n_cf_reward", "n_cf_dyna", "n_context_features", ] xticklabels = [ "state space size", "action\nspace \nsize", "$n_{cf, reward}$", "$n_{cf,dynamics}$", "$n_{cf}$", ] figtitle = "Environments" N = len(cols_plot) theta = radar_factory(N, frame="polygon") figsize = (10, 2.5) dpi = 250 fig, axs = plt.subplots( figsize=figsize, nrows=1, ncols=len(env_types), subplot_kw=dict(projection="radar"), dpi=dpi, ) # fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.99, bottom=0.01) # Plot the four cases from the example data on separate axes for ax, env_type in zip(axs.flat, env_types): D = data[data["env_type"] == env_type] # type: ignore [call-overload] labels = D["env_name"].to_list() color_palette_name = "colorblind" n = len(D) colors = sns.color_palette(color_palette_name, n) plot_data = D[cols_plot].to_numpy() ax.set_rgrids([0.2, 0.4, 0.6, 0.8]) title = env_type.replace("_", " ") if title == "misc": title = "RNA + Mario" ax.set_title( title, weight="normal", # size="medium", # position=(0.5, 0.25), transform=ax.transAxes, horizontalalignment="center", verticalalignment="center", pad=15, fontsize=12, ) for i, (d, color) in enumerate(zip(plot_data, colors)): ax.plot(theta, d, color=color, label=labels[i]) ax.fill(theta, d, facecolor=color, alpha=0.25) ax.set_varlabels( xticklabels, horizontalalignment="center", verticalalignment="center" ) # ax.legend(loc=(0.25, -.5), labelspacing=0.1, fontsize='small') rticks = np.linspace(0, 1, 5) ax.set_rticks(rticks) plt.setp(ax.get_yticklabels(), visible=False) # add legend relative to top-left plot # labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5') # legend = axs[0, 0].legend(labels, loc=(0.9, .95), # labelspacing=0.1, fontsize='small') # fig.text(0.5, 0.965, figtitle, # horizontalalignment='center', color='black', weight='bold', # size='large') fig.set_tight_layout(True) figfname = Path(__file__).parent / "generated" / "radar_env_space.pdf" fig.savefig(figfname, bbox_inches="tight", dpi=300) plt.show()
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CARL-main/carl/utils/doc_building/plotting.py
from typing import Any, Dict, List, Tuple import numpy as np from matplotlib.lines import Line2D from matplotlib.patches import Circle, RegularPolygon from matplotlib.path import Path from matplotlib.projections import register_projection from matplotlib.projections.polar import PolarAxes from matplotlib.spines import Spine from matplotlib.transforms import Affine2D def radar_factory(num_vars: int, frame: str = "circle") -> np.ndarray: """ Create a radar chart with `num_vars` axes. This function creates a RadarAxes projection and registers it. https://matplotlib.org/stable/gallery/specialty_plots/radar_chart.html Parameters ---------- num_vars : int Number of variables for radar chart. frame : {'circle', 'polygon'} Shape of frame surrounding axes. """ # calculate evenly-spaced axis angles theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False) class RadarAxes(PolarAxes): name = "radar" # use 1 line segment to connect specified points RESOLUTION = 1 def __init__(self, *args: Tuple, **kwargs: Dict): super().__init__(*args, **kwargs) # rotate plot such that the first axis is at the top self.set_theta_zero_location("N") def fill(self, *args: Tuple, closed: bool = True, **kwargs: Dict) -> Any: """Override fill so that line is closed by default""" return super().fill(closed=closed, *args, **kwargs) def plot(self, *args: Tuple, **kwargs: Dict) -> None: """Override plot so that line is closed by default""" lines = super().plot(*args, **kwargs) for line in lines: self._close_line(line) def _close_line(self, line: Line2D) -> None: x, y = line.get_data() # FIXME: markers at x[0], y[0] get doubled-up if x[0] != x[-1]: x = np.append(x, x[0]) y = np.append(y, y[0]) line.set_data(x, y) def set_varlabels(self, labels: List[str], **kwargs: Dict) -> None: self.set_thetagrids(np.degrees(theta), labels, **kwargs) def _gen_axes_patch(self) -> Any: # The Axes patch must be centered at (0.5, 0.5) and of radius 0.5 # in axes coordinates. if frame == "circle": return Circle((0.5, 0.5), 0.5) elif frame == "polygon": return RegularPolygon((0.5, 0.5), num_vars, radius=0.5, edgecolor="k") else: raise ValueError("Unknown value for 'frame': %s" % frame) def _gen_axes_spines(self) -> Dict[str, Spine]: if frame == "circle": return super()._gen_axes_spines() elif frame == "polygon": # spine_type must be 'left'/'right'/'top'/'bottom'/'circle'. spine = Spine( axes=self, spine_type="circle", path=Path.unit_regular_polygon(num_vars), ) # unit_regular_polygon gives a polygon of radius 1 centered at # (0, 0) but we want a polygon of radius 0.5 centered at (0.5, # 0.5) in axes coordinates. spine.set_transform( Affine2D().scale(0.5).translate(0.5, 0.5) + self.transAxes ) return {"polar": spine} else: raise ValueError("Unknown value for 'frame': %s" % frame) register_projection(RadarAxes) return theta
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CARL-main/carl/utils/doc_building/print_tables.py
if __name__ == "__main__": from typing import List from pathlib import Path import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from matplotlib.offsetbox import AnchoredText import carl.envs def plot_context_feature_freq( context_feature_names: List[str], fname: str = "" ) -> None: filter_cf_names = True if filter_cf_names: aliases = { "mass": ["mass", "m"], "geometry": ["length", "l", "height", "h", "width", "w", "radius"], "power": ["power"], "gravity": ["gravity", "g"], "force": ["force"], "position": ["position", "distance"], "velocity": ["velocity", "speed"], "torque": ["torque"], "damping": ["damping"], "friction": ["friction"], } cfs = [] for cf in context_feature_names: cf = cf.lower() for alias, alias_values in aliases.items(): longs = [a for a in alias_values if len(a) > 2] shorts = [a for a in alias_values if len(a) <= 2] if np.any([a in cf for a in longs]) or np.any( [ cf == a or cf[-len(a) :] == a or cf[: len(a) + 1] == a + "_" or cf == a for a in shorts ] ): cf = alias cfs.append(cf) context_feature_names = cfs cf_names_unique, counts = np.unique(context_feature_names, return_counts=True) counts_orig = counts ids = np.argsort(counts) cf_names_unique = cf_names_unique[ids] counts = counts[ids] filter_single_occurrences = True cf_names_single_occurrences = [] if filter_single_occurrences: ids = counts == 1 cf_names_single_occurrences = cf_names_unique[ids] cf_names_single_occurrences.sort() cf_names_unique = cf_names_unique[~ids] counts = counts[~ids] # context feature frequency fig = plt.figure(figsize=(5, 7), dpi=200) ax = fig.add_subplot(111) ax.barh(cf_names_unique, counts) ax.set_yticklabels(cf_names_unique, ha="right", fontsize=8) ax.set_title( f"Context Feature Frequency (lazily filtered, $n = {np.sum(counts_orig)}$)", fontsize=10, ) ax.grid(axis="x", which="both") if filter_single_occurrences: text = "Single occurrences:\n" + "\n".join( [f"{cf}" for cf in cf_names_single_occurrences] ) at2 = AnchoredText( text, loc="lower right", prop=dict(size=8), frameon=False, # bbox_to_anchor=(0., 1.), # bbox_transform=ax.transAxes ) at2.patch.set_boxstyle("round,pad=0.,rounding_size=0.2") ax.add_artist(at2) fig.set_tight_layout(True) if fname: fig.savefig(fname, bbox_inches="tight") plt.show() global_vars = vars(carl.envs) vars = { k: v for k, v in global_vars.items() if "Env" in k or "Meta" in k or "CARL" in k } env_names = [ n for n in vars.keys() if "bounds" not in n and "defaults" not in n and "mask" not in n ] env_context_feature_names = {} context_feature_names = [] dfs = [] n_context_features = [] for env_name in env_names: defaults = pd.Series( getattr(eval(getattr(carl.envs, env_name).__module__), "DEFAULT_CONTEXT") ) # pd.Series(vars[env_name + "_defaults"]) n_context_features.append(len(defaults)) bounds = getattr( eval(getattr(carl.envs, env_name).__module__), "CONTEXT_BOUNDS" ) # vars[env_name + "_bounds"] print_bounds = {} for k, v in bounds.items(): lower = v[0] upper = v[1] datatype = v[2] if datatype == "categorical": pass else: datatype = datatype.__name__ print_bounds[k] = (lower, upper, datatype) # print_bounds = {k: (v[0], v[1], v[2].__name__) for k, v in bounds.items()} bounds = pd.Series(print_bounds) defaults.sort_index(inplace=True) bounds.sort_index(inplace=True) df = pd.DataFrame() df["Default"] = defaults df["Bounds"] = [b[:2] for b in bounds] df["Bounds"].replace(to_replace=(None, None), value="-", inplace=True) df["Bounds"][df["Bounds"] == (None, None)] = "-" df["Type"] = [b[2] for b in bounds] rows = df.index context_feature_names.extend(rows) # if "Acro" in env_name: # special_format_cols = ["max_velocity_1", "max_velocity_2"] # for b in bounds: # print(f"({b[0]}, {b[1]})", type(b[0])) # index = [r.lower().replace("_", " ") for r in rows] # tuples = [(env_name, ind) for ind in index] # index = pd.MultiIndex.from_tuples(tuples, names=["Environment", "Context Feature"]) # df.index = index dfs.append(df) env_context_feature_names[env_name] = list(defaults.keys()) df_cf_defbounds = pd.concat(dfs) # Requires latex \usepackage{booktabs} bold_rows = False table_str = df_cf_defbounds.to_latex( header=True, index=True, index_names=True, float_format="{:0.2f}".format, bold_rows=bold_rows, caption=( "Context Features: Defaults and Bounds", "Context Features: Defaults and bounds for each environment.", ), label="tab:context_features_defaults_bounds", # position="c",? ) df_cf_defbounds_fname = ( Path(__file__).parent / "generated" / "context_features_defaults_bounds.tex" ) df_cf_defbounds_fname.parent.mkdir(exist_ok=True, parents=True) with open(df_cf_defbounds_fname, "w") as file: file.write(table_str) for env_name, df in zip(env_names, dfs): # index = [ind[1] for ind in df.index] # no multi-index anymore # df.index = index df.index.name = "Context Feature" df.reset_index(level=0, inplace=True) table_str = df.to_latex( header=True, index=False, index_names=True, float_format="{:0.2f}".format, bold_rows=bold_rows, caption=( f"{env_name}", #: Context Features with Defaults, Bounds and Types", f"{env_name}", #: Context Features with Defaults, Bounds and Types" ), label=f"tab:context_features_defaults_bounds_{env_name}", # position="c",? ) table_str = table_str.replace("{table}", "{subtable}") table_str = table_str.replace( r"\begin{subtable}", r"\begin{subtable}{0.4\textwidth}" ) # print(table_str) fname = ( df_cf_defbounds_fname.parent / f"context_features_defaults_bounds_{env_name}.tex" ) with open(fname, "w") as file: file.write(table_str) # plot_context_feature_freq(context_feature_names=context_feature_names, fname="utils/context_feature_freq.png") def plot_statistics( env_names: List[str], n_context_features: int, fname: str = "" ) -> None: fig = plt.figure(figsize=(5, 7), dpi=200) ax = fig.add_subplot(111) ax.barh(env_names, n_context_features) ax.set_yticklabels(env_names, ha="right", fontsize=8) ax.set_title("TODO", fontsize=10) ax.grid(axis="x", which="both") fig.set_tight_layout(True) if fname: fig.savefig(fname, bbox_inches="tight") plt.show() # collect size of state space calc_new = False if calc_new: state_space_sizes = [] action_space_sizes = [] for env_name in env_names: env = eval(env_name)(hide_context=True) state = env.observation_space action_space = env.action_space env.close() state_space_sizes.append(state.shape) action_space_sizes.append(action_space.shape) print(env_name, state.shape) else: env_names = [ "CARLMountainCarEnv", "CARLPendulumEnv", "CARLAcrobotEnv", "CARLCartPoleEnv", "CARLMountainCarContinuousEnv", "CARLLunarLanderEnv", "CARLVehicleRacingEnv", "CARLBipedalWalkerEnv", "CARLAnt", "CARLHalfcheetah", "CARLHumanoid", "CARLFetch", "CARLGrasp", "CARLUr5e", ] # hide_context = False state_space_sizes = [ (13,), (8,), (15,), (10,), (12,), (24,), (96, 96, 3), (44,), (94,), (29,), (304,), (110,), (141,), (75,), ] # hide_context = True state_space_sizes = [ (2,), (3,), (6,), (4,), (2,), (8,), (96, 96, 3), (24,), (87,), (23,), (299,), (101,), (132,), (66,), ] # , (11,), (64, 64, 3)] action_space_sizes = [ (3,), (1,), (3,), (2,), (1,), (4,), (3,), (4,), (8,), (6,), (17,), (10,), (19,), (6,), ] fname = df_cf_defbounds_fname.parent / "env_statistics.png" env_names.append("CARLRnaDesignEnv") n_context_features.append(5) state_space_sizes.append((11,)) action_space_sizes.append((8,)) # 2 types with 4 actions env_context_feature_names["CARLRnaDesignEnv"] = [ "mutation_threshold", "reward_exponent", "state_radius", "dataset", "target_structure_ids", ] env_names.append("CARLMarioEnv") n_context_features.append(3) state_space_sizes.append((64, 64, 3)) action_space_sizes.append((10,)) env_context_feature_names["CARLMarioEnv"] = ["level_index", "noise", "mario_state"] # plot_statistics(env_names, n_context_features, fname=fname) s_sizes = [s[0] for s in state_space_sizes if len(s) == 1] # plot_statistics(env_names, s_sizes) a_sizes = [s[0] for s in action_space_sizes] # plot_statistics(env_names, a_sizes) sns.set_style("darkgrid") sns.set_context("paper") n_envs = len(env_names) n_axes = 2 figsize = (3, 2 * n_axes) dpi = 200 fig = plt.figure(figsize=figsize, dpi=dpi) axes = fig.subplots(nrows=n_axes, ncols=1) ax = axes[0] ax = sns.histplot(x=s_sizes, ax=ax, bins=n_envs) ax.set_xlabel("State Space Size") ax.set_ylabel("$n_{{envs}}$") ax = axes[1] ax = sns.histplot(x=a_sizes, ax=ax, bins=n_envs) ax.set_xlabel("Action Space Size") ax.set_ylabel("$n_{{envs}}$") fig.set_tight_layout(True) fig.savefig(fname, bbox_inches="tight", dpi=300) plt.show()
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CARL-main/carl/utils/doc_building/render_brax_env.py
if __name__ == "__main__": from typing import List import brax import jax from brax import envs from brax.io import html from IPython.display import HTML env_name = "fetch" # @param ['ant', 'humanoid', 'fetch', 'grasp', 'halfcheetah', 'ur5e', 'reacher'] env_fn = envs.create_fn(env_name=env_name) env = env_fn() state = env.reset(rng=jax.random.PRNGKey(seed=1)) def visualize(sys: brax.System, qps: List[brax.QP]) -> HTML: """Renders a 3D visualization of the environment.""" return HTML(html.render(sys, qps)) # htmlrender = visualize(env.sys, [state.qp]) html.save_html(path=f"tmp/env_render/{env_name}.html", sys=env.sys, qps=[state.qp])
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CARL-main/docs/conf.py
import os, sys sys.path.insert(0, os.path.abspath("..")) import automl_sphinx_theme # Must come after the path injection above from carl import copyright, author, version, name options = {"copyright": copyright, "author": author, "version": version, "versions": { f"v{version} (stable)": "#", }, "name": name, "html_theme_options": { "github_url": "https://github.com/automl/automl_sphinx_theme", "twitter_url": "https://twitter.com/automl_org?lang=de", }, #this is here to exclude the examples gallery since they are not documented "extensions": ["myst_parser", "sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.napoleon", # Enables to understand NumPy docstring # "numpydoc", "sphinx.ext.autosummary", "sphinx.ext.autosectionlabel", "sphinx_autodoc_typehints", "sphinx.ext.doctest", ] } # Import conf.py from the automl theme automl_sphinx_theme.set_options(globals(), options)
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CARL-main/docs/themes/smac/docs-navbar.html
<div class="container-xl"> <div id="navbar-start"> {% for navbar_item in theme_navbar_start %} {% include navbar_item %} {% endfor %} </div> <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbar-collapsible" aria-controls="navbar-collapsible" aria-expanded="false" aria-label="{{ _('Toggle navigation') }}"> <span class="navbar-toggler-icon"></span> </button> {% set navbar_class, navbar_align = navbar_align_class() %} <div id="navbar-collapsible" class="{{ navbar_class }} collapse navbar-collapse"> <div id="navbar-center" class="{{ navbar_align }}"> {% for navbar_item in theme_navbar_center %} <div class="navbar-center-item"> {% include navbar_item %} </div> {% endfor %} </div> <div id="navbar-end"> {% for navbar_item in theme_navbar_end %} <div class="navbar-end-item"> {% include navbar_item %} </div> {% endfor %} </div> </div> </div>
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CARL-main/docs/themes/smac/docs-sidebar.html
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CARL-main/docs/themes/smac/footer.html
<footer class="footer mt-5 mt-md-0"> <div class="container"> {% for footer_item in theme_footer_items %} <div class="footer-item"> {% include footer_item %} </div> {% endfor %} </div> </footer>
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CARL-main/docs/themes/smac/gc.html
<li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown">{{ _('Site') }} <b class="caret"></b></a> <ul class="dropdown-menu globaltoc">{{ toctree(maxdepth=10) }}</ul> </li>
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CARL-main/docs/themes/smac/icon-links.html
{%- macro icon_link_nav_item(url, icon, name) -%} {%- if url | length > 2 %} <li class="nav-item"> <a class="nav-link" href="{{ url }}" rel="noopener" target="_blank" title="{{ _(name) }}"> <span><i class="{{ icon }}"></i></span> <label class="sr-only">{{ _(name) }}</label> </a> </li> {%- endif -%} {%- endmacro -%} <ul id="navbar-icon-links" class="navbar-nav" aria-label="{{ _(theme_icon_links_label) }}"> {%- block icon_link_shortcuts -%} {{ icon_link_nav_item(theme_github_url, "fab fa-github-square", "GitHub") -}} {{ icon_link_nav_item(theme_gitlab_url, "fab fa-gitlab", "GitLab") -}} {{ icon_link_nav_item(theme_bitbucket_url, "fab fa-bitbucket", "Bitbucket") -}} {{ icon_link_nav_item(theme_twitter_url, "fab fa-twitter-square", "Twitter") -}} {% endblock -%} {%- for icon_link in theme_icon_links -%} {{ icon_link_nav_item(icon_link["url"], icon_link["icon"], icon_link["name"]) -}} {%- endfor %} </ul>
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CARL-main/docs/themes/smac/layout.html
{%- extends "basic/layout.html" %} {%- import "static/webpack-macros.html" as _webpack with context %} {%- block css %} {{ _webpack.head_pre_bootstrap() }} {{ _webpack.head_pre_icons() }} {% block fonts %} {{ _webpack.head_pre_fonts() }} {% endblock %} {{- css() }} {{ _webpack.head_js_preload() }} {%- endblock %} {%- block extrahead %} <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="docsearch:language" content="{{ language }}"> {% for favicon in theme_favicons %} {% if favicon.href[:4] == 'http'%} <link rel="{{ favicon.rel }}" sizes="{{ favicon.sizes }}" href="{{ favicon.href }}"> {% else %} <link rel="{{ favicon.rel }}" sizes="{{ favicon.sizes }}" href="{{ pathto('_static/' + favicon.href, 1) }}"> {% endif %} {% endfor %} <!-- Google Analytics --> {{ generate_google_analytics_script(id=theme_google_analytics_id) }} {%- endblock %} {# Silence the sidebar's, relbar's #} {% block header %}{% endblock %} {% block relbar1 %}{% endblock %} {% block relbar2 %}{% endblock %} {% block sidebarsourcelink %}{% endblock %} {% block body_tag %} <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80"> {%- endblock %} {%- block content %} {# Added to support a banner with an alert #} <div class="container-fluid" id="banner"></div> {% block docs_navbar %} <nav class="navbar navbar-light navbar-expand-lg bg-light fixed-top bd-navbar" id="navbar-main"> {%- include "docs-navbar.html" %} </nav> {% endblock %} <div class="container-xl"> <div class="row"> {% block docs_sidebar %} {% if sidebars %} <!-- Only show if we have sidebars configured, else just a small margin --> <div class="col-12 col-md-3 bd-sidebar"> {%- for sidebartemplate in sidebars %} {%- include sidebartemplate %} {%- endfor %} </div> {% else %} <div class="col-12 col-md-1 col-xl-2 bd-sidebar no-sidebar"></div> {% endif %} {% endblock %} {% block docs_toc %} <div class="d-none d-xl-block col-xl-2 bd-toc"> {% if meta is defined and not (meta is not none and 'notoc' in meta) %} {% for toc_item in theme_page_sidebar_items %} <div class="toc-item"> {% include toc_item %} </div> {% endfor %} {% endif %} </div> {% endblock %} {% block docs_main %} {% if sidebars %} {% set content_col_class = "col-md-9 col-xl-7" %} {% else %} {% set content_col_class = "col-md-11 col-xl-8" %} {% endif %} <main class="col-12 {{ content_col_class }} py-md-5 pl-md-5 pr-md-4 bd-content" role="main"> {% block docs_body %} <div> {% block body %} {% endblock %} </div> {% endblock %} {% if theme_show_prev_next %} {% include "templates/prev-next.html" %} {% endif %} </main> {% endblock %} </div> </div> {%- block scripts_end %} {{ _webpack.body_post() }} {%- endblock %} {%- endblock %} {%- block footer %} {%- include "footer.html" %} {%- endblock %}
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CARL-main/docs/themes/smac/search-field.html
<form class="bd-search align-items-center" action="{{ pathto('search') }}" method="get" style="width: 100%;"> <i class="icon fas fa-search"></i> <input type="search" class="form-control" name="q" id="search-input" placeholder="{{ _(theme_search_bar_text) }}" aria-label="{{ theme_search_bar_text }}" autocomplete="off" > </form>
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CARL-main/docs/themes/smac/smac_theme.py
""" Bootstrap-based sphinx theme from the PyData community """ # flake8: noqa: E402 # mypy: ignore-errors import os from sphinx.errors import ExtensionError from sphinx.util import logging from sphinx.environment.adapters.toctree import TocTree from sphinx import addnodes import jinja2 from bs4 import BeautifulSoup as bs __version__ = "0.7.1" logger = logging.getLogger(__name__) def update_config(app, env): theme_options = app.config["html_theme_options"] if theme_options.get("search_bar_position") == "navbar": logger.warn( ( "Deprecated config `search_bar_position` used." "Use `search-field.html` in `navbar_end` template list instead." ) ) if not isinstance(theme_options.get("icon_links", []), list): raise ExtensionError( ( "`icon_links` must be a list of dictionaries, you provided " f"type {type(theme_options.get('icon_links'))}." ) ) def update_templates(app, pagename, templatename, context, doctree): """Update template names for page build.""" template_sections = [ "theme_navbar_start", "theme_navbar_center", "theme_navbar_end", "theme_footer_items", "theme_page_sidebar_items", "sidebars", ] for section in template_sections: if context.get(section): # Break apart `,` separated strings so we can use , in the defaults if isinstance(context.get(section), str): context[section] = [ ii.strip() for ii in context.get(section).split(",") ] # Add `.html` to templates with no suffix for ii, template in enumerate(context.get(section)): if not os.path.splitext(template)[1]: context[section][ii] = template + ".html" def add_toctree_functions(app, pagename, templatename, context, doctree): """Add functions so Jinja templates can add toctree objects.""" def generate_nav_html(kind, startdepth=None, **kwargs): """ Return the navigation link structure in HTML. Arguments are passed to Sphinx "toctree" function (context["toctree"] below). We use beautifulsoup to add the right CSS classes / structure for bootstrap. See https://www.sphinx-doc.org/en/master/templating.html#toctree. Parameters ---------- kind : ["navbar", "sidebar", "raw"] The kind of UI element this toctree is generated for. startdepth : int The level of the toctree at which to start. By default, for the navbar uses the normal toctree (`startdepth=0`), and for the sidebar starts from the second level (`startdepth=1`). kwargs: passed to the Sphinx `toctree` template function. Returns ------- HTML string (if kind in ["navbar", "sidebar"]) or BeautifulSoup object (if kind == "raw") """ if startdepth is None: startdepth = 0 # 1 if kind == "sidebar" else 0 if startdepth == 0: toc_sphinx = context["toctree"](**kwargs) else: # select the "active" subset of the navigation tree for the sidebar toc_sphinx = index_toctree(app, pagename, startdepth, **kwargs) soup = bs(toc_sphinx, "html.parser") # pair "current" with "active" since that's what we use w/ bootstrap for li in soup("li", {"class": "current"}): li["class"].append("active") # Remove navbar/sidebar links to sub-headers on the page for li in soup.select("li"): # Remove if li.find("a"): href = li.find("a")["href"] if "#" in href and href != "#": li.decompose() if kind == "navbar": # Add CSS for bootstrap for li in soup("li"): li["class"].append("nav-item") li.find("a")["class"].append("nav-link") # only select li items (not eg captions) out = "\n".join([ii.prettify() for ii in soup.find_all("li")]) elif kind == "sidebar": # Add bootstrap classes for first `ul` items for ul in soup("ul", recursive=False): ul.attrs["class"] = ul.attrs.get("class", []) + ["nav", "bd-sidenav"] # Add icons and labels for collapsible nested sections _add_collapse_checkboxes(soup) out = soup.prettify() elif kind == "raw": out = soup return out def generate_toc_html(kind="html"): """Return the within-page TOC links in HTML.""" if "toc" not in context: return "" soup = bs(context["toc"], "html.parser") # Add toc-hN + visible classes def add_header_level_recursive(ul, level): if ul is None: return if level <= (context["theme_show_toc_level"] + 1): ul["class"] = ul.get("class", []) + ["visible"] for li in ul("li", recursive=False): li["class"] = li.get("class", []) + [f"toc-h{level}"] add_header_level_recursive(li.find("ul", recursive=False), level + 1) add_header_level_recursive(soup.find("ul"), 1) # Add in CSS classes for bootstrap for ul in soup("ul"): ul["class"] = ul.get("class", []) + ["nav", "section-nav", "flex-column"] for li in soup("li"): li["class"] = li.get("class", []) + ["nav-item", "toc-entry"] if li.find("a"): a = li.find("a") a["class"] = a.get("class", []) + ["nav-link"] # If we only have one h1 header, assume it's a title h1_headers = soup.select(".toc-h1") if len(h1_headers) == 1: title = h1_headers[0] # If we have no sub-headers of a title then we won't have a TOC if not title.select(".toc-h2"): out = "" else: out = title.find("ul").prettify() # Else treat the h1 headers as sections else: out = soup.prettify() # Return the toctree object if kind == "html": return out else: return soup def get_nav_object(maxdepth=None, collapse=False, includehidden=True, **kwargs): """Return a list of nav links that can be accessed from Jinja. Parameters ---------- maxdepth: int How many layers of TocTree will be returned collapse: bool Whether to only include sub-pages of the currently-active page, instead of sub-pages of all top-level pages of the site. kwargs: key/val pairs Passed to the `toctree` Sphinx method """ if context["master_doc"] == pagename: logger.warn("`get_nav_object` is deprecated and will be removed in v0.7.0") toc_sphinx = context["toctree"]( maxdepth=maxdepth, collapse=collapse, includehidden=includehidden, **kwargs ) soup = bs(toc_sphinx, "html.parser") # # If no toctree is defined (AKA a single-page site), skip this # if toctree is None: # return [] nav_object = soup_to_python(soup, only_pages=True) return nav_object def get_page_toc_object(): """Return a list of within-page TOC links that can be accessed from Jinja.""" if context["master_doc"] == pagename: logger.warn( ("`get_page_toc_object` is deprecated and will be " "removed in v0.7.0") ) if "toc" not in context: return "" soup = bs(context["toc"], "html.parser") try: toc_object = soup_to_python(soup, only_pages=False) except Exception: return [] # If there's only one child, assume we have a single "title" as top header # so start the TOC at the first item's children (AKA, level 2 headers) if len(toc_object) == 1: return toc_object[0]["children"] else: return toc_object def navbar_align_class(): """Return the class that aligns the navbar based on config.""" align = context.get("theme_navbar_align", "content") align_options = { "content": ("col-lg-9", "mr-auto"), "left": ("", "mr-auto"), "right": ("", "ml-auto"), } if align not in align_options: raise ValueError( ( "Theme optione navbar_align must be one of" f"{align_options.keys()}, got: {align}" ) ) return align_options[align] def generate_google_analytics_script(id): """Handle the two types of google analytics id.""" if id: if "G-" in id: script = f""" <script async src='https://www.googletagmanager.com/gtag/js?id={id}' ></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){{ dataLayer.push(arguments); }} gtag('js', new Date()); gtag('config', '{id}'); </script> """ else: script = f""" <script async src='https://www.google-analytics.com/analytics.js' ></script> <script> window.ga = window.ga || function () {{ (ga.q = ga.q || []).push(arguments) }}; ga.l = +new Date; ga('create', '{id}', 'auto'); ga('set', 'anonymizeIp', true); ga('send', 'pageview'); </script> """ soup = bs(script, "html.parser") return soup return "" context["generate_nav_html"] = generate_nav_html context["generate_toc_html"] = generate_toc_html context["get_nav_object"] = get_nav_object context["get_page_toc_object"] = get_page_toc_object context["navbar_align_class"] = navbar_align_class context["generate_google_analytics_script"] = generate_google_analytics_script def _add_collapse_checkboxes(soup): # based on https://github.com/pradyunsg/furo toctree_checkbox_count = 0 for element in soup.find_all("li", recursive=True): # We check all "li" elements, to add a "current-page" to the correct li. classes = element.get("class", []) # Nothing more to do, unless this has "children" if not element.find("ul"): continue # Add a class to indicate that this has children. element["class"] = classes + ["has-children"] # We're gonna add a checkbox. toctree_checkbox_count += 1 checkbox_name = f"toctree-checkbox-{toctree_checkbox_count}" # Add the "label" for the checkbox which will get filled. if soup.new_tag is None: continue label = soup.new_tag("label", attrs={"for": checkbox_name}) label.append(soup.new_tag("i", attrs={"class": "fas fa-chevron-down"})) element.insert(1, label) # Add the checkbox that's used to store expanded/collapsed state. checkbox = soup.new_tag( "input", attrs={ "type": "checkbox", "class": ["toctree-checkbox"], "id": checkbox_name, "name": checkbox_name, }, ) # if this has a "current" class, be expanded by default # (by checking the checkbox) if "current" in classes: checkbox.attrs["checked"] = "" element.insert(1, checkbox) def _get_local_toctree_for( self: TocTree, indexname: str, docname: str, builder, collapse: bool, **kwargs ): """Return the "local" TOC nodetree (relative to `indexname`).""" # this is a copy of `TocTree.get_toctree_for`, but where the sphinx version # always uses the "master" doctree: # doctree = self.env.get_doctree(self.env.config.master_doc) # we here use the `indexname` additional argument to be able to use a subset # of the doctree (e.g. starting at a second level for the sidebar): # doctree = app.env.tocs[indexname].deepcopy() doctree = self.env.tocs[indexname].deepcopy() toctrees = [] if "includehidden" not in kwargs: kwargs["includehidden"] = True if "maxdepth" not in kwargs or not kwargs["maxdepth"]: kwargs["maxdepth"] = 0 else: kwargs["maxdepth"] = int(kwargs["maxdepth"]) kwargs["collapse"] = collapse for toctreenode in doctree.traverse(addnodes.toctree): toctree = self.resolve(docname, builder, toctreenode, prune=True, **kwargs) if toctree: toctrees.append(toctree) if not toctrees: return None result = toctrees[0] for toctree in toctrees[1:]: result.extend(toctree.children) return result def index_toctree(app, pagename: str, startdepth: int, collapse: bool = False, **kwargs): """ Returns the "local" (starting at `startdepth`) TOC tree containing the current page, rendered as HTML bullet lists. This is the equivalent of `context["toctree"](**kwargs)` in sphinx templating, but using the startdepth-local instead of global TOC tree. """ # this is a variant of the function stored in `context["toctree"]`, which is # defined as `lambda **kwargs: self._get_local_toctree(pagename, **kwargs)` # with `self` being the HMTLBuilder and the `_get_local_toctree` basically # returning: # return self.render_partial(TocTree(self.env).get_toctree_for( # pagename, self, collapse, **kwargs))['fragment'] if "includehidden" not in kwargs: kwargs["includehidden"] = False if kwargs.get("maxdepth") == "": kwargs.pop("maxdepth") toctree = TocTree(app.env) ancestors = toctree.get_toctree_ancestors(pagename) try: indexname = ancestors[-startdepth] except IndexError: # eg for index.rst, but also special pages such as genindex, py-modindex, search # those pages don't have a "current" element in the toctree, so we can # directly return an emtpy string instead of using the default sphinx # toctree.get_toctree_for(pagename, app.builder, collapse, **kwargs) return "" toctree_element = _get_local_toctree_for( toctree, indexname, pagename, app.builder, collapse, **kwargs ) return app.builder.render_partial(toctree_element)["fragment"] def soup_to_python(soup, only_pages=False): """ Convert the toctree html structure to python objects which can be used in Jinja. Parameters ---------- soup : BeautifulSoup object for the toctree only_pages : bool Only include items for full pages in the output dictionary. Exclude anchor links (TOC items with a URL that starts with #) Returns ------- nav : list of dicts The toctree, converted into a dictionary with key/values that work within Jinja. """ # toctree has this structure (caption only for toctree, not toc) # <p class="caption">...</span></p> # <ul> # <li class="toctree-l1"><a href="..">..</a></li> # <li class="toctree-l1"><a href="..">..</a></li> # ... def extract_level_recursive(ul, navs_list): for li in ul.find_all("li", recursive=False): ref = li.a url = ref["href"] title = "".join(map(str, ref.contents)) active = "current" in li.get("class", []) # If we've got an anchor link, skip it if we wish if only_pages and "#" in url and url != "#": continue # Converting the docutils attributes into jinja-friendly objects nav = {} nav["title"] = title nav["url"] = url nav["active"] = active navs_list.append(nav) # Recursively convert children as well nav["children"] = [] ul = li.find("ul", recursive=False) if ul: extract_level_recursive(ul, nav["children"]) navs = [] for ul in soup.find_all("ul", recursive=False): extract_level_recursive(ul, navs) return navs # ----------------------------------------------------------------------------- def setup_edit_url(app, pagename, templatename, context, doctree): """Add a function that jinja can access for returning the edit URL of a page.""" def get_edit_url(): """Return a URL for an "edit this page" link.""" file_name = f"{pagename}{context['page_source_suffix']}" # Make sure that doc_path has a path separator only if it exists (to avoid //) doc_path = context.get("doc_path", "") if doc_path and not doc_path.endswith("/"): doc_path = f"{doc_path}/" default_provider_urls = { "bitbucket_url": "https://bitbucket.org", "github_url": "https://github.com", "gitlab_url": "https://gitlab.com", } edit_url_attrs = {} # ensure custom URL is checked first, if given url_template = context.get("edit_page_url_template") if url_template is not None: if "file_name" not in url_template: raise ExtensionError( "Missing required value for `use_edit_page_button`. " "Ensure `file_name` appears in `edit_page_url_template`: " f"{url_template}" ) edit_url_attrs[("edit_page_url_template",)] = url_template edit_url_attrs.update( { ("bitbucket_user", "bitbucket_repo", "bitbucket_version"): ( "{{ bitbucket_url }}/{{ bitbucket_user }}/{{ bitbucket_repo }}" "/carl/{{ bitbucket_version }}" "/{{ doc_path }}{{ file_name }}?mode=edit" ), ("github_user", "github_repo", "github_version"): ( "{{ github_url }}/{{ github_user }}/{{ github_repo }}" "/edit/{{ github_version }}/{{ doc_path }}{{ file_name }}" ), ("gitlab_user", "gitlab_repo", "gitlab_version"): ( "{{ gitlab_url }}/{{ gitlab_user }}/{{ gitlab_repo }}" "/-/edit/{{ gitlab_version }}/{{ doc_path }}{{ file_name }}" ), } ) doc_context = dict(default_provider_urls) doc_context.update(context) doc_context.update(doc_path=doc_path, file_name=file_name) for attrs, url_template in edit_url_attrs.items(): if all(doc_context.get(attr) not in [None, "None"] for attr in attrs): return jinja2.Template(url_template).render(**doc_context) raise ExtensionError( "Missing required value for `use_edit_page_button`. " "Ensure one set of the following in your `html_context` " f"configuration: {sorted(edit_url_attrs.keys())}" ) context["get_edit_url"] = get_edit_url # Ensure that the max TOC level is an integer context["theme_show_toc_level"] = int(context.get("theme_show_toc_level", 1)) # ----------------------------------------------------------------------------- from distutils.version import LooseVersion from docutils import nodes import sphinx from sphinx.writers.html5 import HTML5Translator from sphinx.util import logging from sphinx.ext.autosummary import autosummary_table class BootstrapHTML5Translator(HTML5Translator): """Custom HTML Translator for a Bootstrap-ified Sphinx layout This is a specialization of the HTML5 Translator of sphinx. Only a couple of functions have been overridden to produce valid HTML to be directly styled with Bootstrap, and fulfill acessibility best practices. """ def __init__(self, *args, **kwds): super().__init__(*args, **kwds) self.settings.table_style = "table" def starttag(self, *args, **kwargs): """ensure an aria-level is set for any heading role""" if kwargs.get("ROLE") == "heading" and "ARIA-LEVEL" not in kwargs: kwargs["ARIA-LEVEL"] = "2" return super().starttag(*args, **kwargs) def visit_table(self, node): # type: (nodes.Element) -> None # copy of sphinx source to *not* add 'docutils' and 'align-default' classes # but add 'table' class # generate_targets_for_table is deprecated in 4.0 if LooseVersion(sphinx.__version__) < LooseVersion("4.0"): self.generate_targets_for_table(node) self._table_row_index = 0 classes = [cls.strip(" \t\n") for cls in self.settings.table_style.split(",")] # we're looking at the 'real_table', which is wrapped by an autosummary if isinstance(node.parent, autosummary_table): classes += ["autosummary"] # classes.insert(0, "docutils") # compat # if 'align' in node: # classes.append('align-%s' % node['align']) tag = self.starttag(node, "table", CLASS=" ".join(classes)) self.body.append(tag) # ----------------------------------------------------------------------------- def get_html_theme_path(): """Return list of HTML theme paths.""" theme_path = os.path.abspath(os.path.dirname(__file__)) return [theme_path] def setup(app): print("hi") theme_path = get_html_theme_path()[0] app.add_html_theme("smac_theme", theme_path) app.set_translator("html", BootstrapHTML5Translator) # Read the Docs uses ``readthedocs`` as the name of the build, and also # uses a special "dirhtml" builder so we need to replace these both with # our custom HTML builder app.set_translator("readthedocs", BootstrapHTML5Translator, override=True) app.set_translator("readthedocsdirhtml", BootstrapHTML5Translator, override=True) app.connect("env-updated", update_config) app.connect("html-page-context", setup_edit_url) app.connect("html-page-context", add_toctree_functions) app.connect("html-page-context", update_templates) # Update templates for sidebar pkgdir = os.path.abspath(os.path.dirname(__file__)) path_templates = os.path.join(pkgdir, "templates") app.config.templates_path.append(path_templates) return {"parallel_read_safe": True, "parallel_write_safe": True}
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CARL-main/docs/themes/smac/title.html
<h4 class="mt-0 mb-0">{{ project }}</h4> <div class="mb-3">v{{ version }}</div>
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CARL-main/docs/themes/smac/static/webpack-macros.html
<!-- these macros are generated by "yarn build:production". do not edit by hand. --> {% macro head_pre_icons() %} <link rel="stylesheet" href="{{ pathto('_static/vendor/fontawesome/5.13.0/css/all.min.css', 1) }}"> <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2', 1) }}"> <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2', 1) }}"> {% endmacro %} {% macro head_pre_fonts() %} {% endmacro %} {% macro head_pre_bootstrap() %} <link href="{{ pathto('_static/css/theme.css', 1) }}" rel="stylesheet"> <link href="{{ pathto('_static/css/index.ac9c05f7c49ca1e1f876c6e36360ea26.css', 1) }}" rel="stylesheet"> {% endmacro %} {% macro head_js_preload() %} <link rel="preload" as="script" href="{{ pathto('_static/js/index.9ea38e314b9e6d9dab77.js', 1) }}"> {% endmacro %} {% macro body_post() %} <script src="{{ pathto('_static/js/index.9ea38e314b9e6d9dab77.js', 1) }}"></script> {% endmacro %}
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CARL-main/docs/themes/smac/static/css/custom.css
.bd-search { margin: 0; padding-left: 0; padding-right: 0; } .bd-search .icon { left: 10px; } #navbar-collapsible { padding: 0; } #navbar-icon-links { margin: 0 15px 0 0; } .navbar-nav { flex-direction: row; } .navbar-light .navbar-nav li a.nav-link { padding: 0 5px 0 0; } @media (max-width:959.98px){.navbar-expand-lg>.container,.navbar-expand-lg>.container-fluid,.navbar-expand-lg>.container-lg,.navbar-expand-lg>.container-md,.navbar-expand-lg>.container-sm,.navbar-expand-lg>.container-xl{ padding-right: 15px; padding-left: 15px; }} p.sphx-glr-signature, p.sphx-glr-timing, .sphx-glr-download, .sphx-glr-download-link-note { display: none; } .sphx-glr-thumbcontainer::before { display: none !important; } .sphx-glr-thumbcontainer { margin: 1px 0 !important; background-color: grey; min-height: auto !important; padding: 0 !important; width: 100% !important; border: 0 !important; } .sphx-glr-thumbcontainer a.headerlink { display: none; } .sphx-glr-thumbcontainer p { margin: 0 !important; } .sphx-glr-thumbcontainer .figure { height: auto !important; margin: 0 !important; padding: 0 !important; width: 100% !important; } .sphx-glr-thumbcontainer .figure p.caption { padding: 0 !important; } .sphx-glr-thumbcontainer .figure p.caption:hover { box-shadow: none !important; -webkit-box-shadow: none !important; } .sphx-glr-thumbcontainer img { display: none !important; } .sphx-glr-thumbcontainer .figure img { display: none !important; } .sphx-glr-thumbcontainer a.internal { position: relative !important; padding: 30px 15px !important; background-color: rgb(128, 128, 128) !important; color: white !important; border: 0 !important; vertical-align: middle; border-radius: 5px; } .sphx-glr-thumbcontainer a.internal:hover { background-color: rgb(60, 60, 60) !important; border: 0 !important; box-shadow: none !important; -webkit-box-shadow: none !important; text-decoration: none; } /* Disable tooltip */ .sphx-glr-thumbcontainer::after { display: none !important; } .sphx-glr-thumbcontainer:hover { border: 0 !important; box-shadow: none !important; } .sphx-glr-thumbcontainer[tooltip]::before, .sphx-glr-thumbcontainer[tooltip]::after, .sphx-glr-thumbcontainer[tooltip]:hover::before, .sphx-glr-thumbcontainer[tooltip]:hover::after { display: none; }
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CARL-main/docs/themes/smac/static/css/index.ac9c05f7c49ca1e1f876c6e36360ea26.css
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CARL-main/docs/themes/smac/static/css/theme.css
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CARL-main/docs/themes/smac/static/vendor/fontawesome/5.13.0/css/all.min.css
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CARL-main/docs/themes/smac/templates/copyright.html
<p class="copyright"> {%- if hasdoc('copyright') %} {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; <a href="{{ path }}">Copyright</a> {{ copyright }}.{% endtrans %}<br> {%- else %} {% trans copyright=copyright|e %}&copy; Copyright {{ copyright }}.{% endtrans %}<br> {%- endif %} </p>
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CARL-main/docs/themes/smac/templates/edit-this-page.html
{% if sourcename is defined and theme_use_edit_page_button==true and page_source_suffix %} {% set src = sourcename.split('.') %} <div class="tocsection editthispage"> <a href="{{ get_edit_url() }}"> <i class="fas fa-pencil-alt"></i> {{ _("Edit this page") }} </a> </div> {% endif %}
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CARL-main/docs/themes/smac/templates/last-updated.html
<p class="last-updated"> {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}<br> </p>
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CARL-main/docs/themes/smac/templates/navbar-icon-links.html
{%- block icon_links -%} {%- include "icon-links.html" with context -%} {%- endblock %}
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CARL-main/docs/themes/smac/templates/navbar-logo.html
{% if logo %} {% if not theme_logo_link %} <a class="navbar-brand" href="{{ pathto(master_doc) }}"> <img src="{{ pathto('_static/' + logo, 1) }}" class="logo" alt="logo"> </a> {% elif theme_logo_link[:4] == 'http' %} <a class="navbar-brand" href="{{ theme_logo_link }}"> <img src="{{ pathto('_static/' + logo, 1) }}" class="logo" alt="logo"> </a> {% else %} <a class="navbar-brand" href="{{ pathto(theme_logo_link) }}"> <img src="{{ pathto('_static/' + logo, 1) }}" class="logo" alt="logo"> </a> {% endif %} {% else %} <a class="navbar-brand" href="{{ pathto(master_doc) }}"> <p class="title">{{ project }}</p> </a> {% endif %}
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CARL-main/docs/themes/smac/templates/navbar-nav.html
<ul id="navbar-main-elements" class="navbar-nav"> {{ generate_nav_html("navbar", maxdepth=1, collapse=True, includehidden=True, titles_only=True) }} {% for external_link in theme_external_links %} <li class="nav-item"> <a class="nav-link nav-external" href="{{ external_link.url }}">{{ _(external_link.name) }}<i class="fas fa-external-link-alt"></i></a> </li> {% endfor %} </ul>
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CARL-main/docs/themes/smac/templates/page-toc.html
{% set page_toc = generate_toc_html() %} {%- if page_toc | length >= 1 %} <div class="tocsection onthispage pt-5 pb-3"> <i class="fas fa-list"></i> {{ _("On this page") }} </div> {%- endif %} <nav id="bd-toc-nav"> {{ page_toc }} </nav>
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CARL-main/docs/themes/smac/templates/prev-next.html
<!-- Previous / next buttons --> <div class='prev-next-area'> {%- if prev %} <a class='left-prev' id="prev-link" href="{{ prev.link|e }}" title="{{ _('previous') }} {{ _('page') }}"> <i class="fas fa-angle-left"></i> <div class="prev-next-info"> <p class="prev-next-subtitle">{{ _("previous") }}</p> <p class="prev-next-title">{{ prev_title or prev.title }}</p> </div> </a> {%- endif %} {%- if next %} <a class='right-next' id="next-link" href="{{ next.link|e }}" title="{{ _('next') }} {{ _('page') }}"> <div class="prev-next-info"> <p class="prev-next-subtitle">{{ _("next") }}</p> <p class="prev-next-title">{{ next_title or next.title }}</p> </div> <i class="fas fa-angle-right"></i> </a> {%- endif %} </div>
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CARL-main/docs/themes/smac/templates/sidebar-ethical-ads.html
{% if READTHEDOCS %} <div id="ethical-ad-placement" class="flat" data-ea-publisher="readthedocs" data-ea-type="readthedocs-sidebar" data-ea-manual="true" ></div> {% endif %}
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CARL-main/docs/themes/smac/templates/sidebar-nav-bs.html
<nav class="bd-links" id="bd-docs-nav" aria-label="{{ _('Main navigation') }}"> <div class="bd-toc-item active"> {{ generate_nav_html("sidebar", maxdepth=theme_navigation_depth|int, collapse=theme_collapse_navigation|tobool, includehidden=True, titles_only=True) }} </div> </nav>
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CARL-main/docs/themes/smac/templates/sphinx-version.html
<p class="sphinx-version"> {% trans sphinx_version=sphinx_version|e %}Created using <a href="http://sphinx-doc.org/">Sphinx</a> {{ sphinx_version }}. Template is modified version of <a href="https://pydata-sphinx-theme.readthedocs.io">PyData Sphinx Theme</a>. {% endtrans %}<br> </p>
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CARL-main/examples/demo_carracing.py
""" Code adapted from gym.envs.box2d.car_racing.py """ from typing import Any import numpy as np import gym import time import pygame from carl.envs.box2d.carl_vehicle_racing import CARLVehicleRacingEnv, VEHICLE_NAMES if __name__ == "__main__": from pyglet.window import key a = np.array([0.0, 0.0, 0.0]) def register_input(): for event in pygame.event.get(): if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: a[0] = -1.0 if event.key == pygame.K_RIGHT: a[0] = +1.0 if event.key == pygame.K_UP: a[1] = +1.0 if event.key == pygame.K_DOWN: a[2] = +0.8 # set 1.0 for wheels to block to zero rotation if event.key == pygame.K_RETURN: global restart restart = True if event.type == pygame.KEYUP: if event.key == pygame.K_LEFT: a[0] = 0 if event.key == pygame.K_RIGHT: a[0] = 0 if event.key == pygame.K_UP: a[1] = 0 if event.key == pygame.K_DOWN: a[2] = 0 contexts = {i: {"VEHICLE": i} for i in range(len(VEHICLE_NAMES))} env = CARLVehicleRacingEnv(contexts=contexts) env.render() record_video = False if record_video: from gym.wrappers.record_video import RecordVideo env = RecordVideo( env=env, video_folder="/tmp/video-test", name_prefix="CARLVehicleRacing" ) isopen = True while isopen: env.reset() total_reward = 0.0 steps = 0 restart = False while True: register_input() s, r, done, info = env.step(a) time.sleep(0.025) total_reward += r if steps % 200 == 0 or done: print("\naction " + str(["{:+0.2f}".format(x) for x in a])) print("step {} total_reward {:+0.2f}".format(steps, total_reward)) steps += 1 isopen = env.render() if done or restart or not isopen: break env.close()
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CARL-main/examples/demo_heuristic_lunarlander.py
from typing import Union, Optional from gym.envs.box2d.lunar_lander import heuristic import gym.envs.box2d.lunar_lander as lunar_lander from carl.envs import CARLLunarLanderEnv def demo_heuristic_lander( env: Union[ CARLLunarLanderEnv, lunar_lander.LunarLander, lunar_lander.LunarLanderContinuous ], seed: Optional[int] = None, render: bool = False, ) -> float: """ Copied from LunarLander """ env.seed(seed) total_reward = 0 steps = 0 env.render() s = env.reset() while True: a = heuristic(env, s) s, r, done, info = env.step(a) total_reward += r if render: still_open = env.render() if not still_open: break if done: # or steps % 20 == 0: # print("observations:", " ".join(["{:+0.2f}".format(x) for x in s])) print("step {} total_reward {:+0.2f}".format(steps, total_reward)) steps += 1 if done: break return total_reward if __name__ == "__main__": env = CARLLunarLanderEnv( hide_context=False, add_gaussian_noise_to_context=True, gaussian_noise_std_percentage=0.1, ) # env.render() # initialize viewer. otherwise weird bug. # env = ll.LunarLander() # env = CustomLunarLanderEnv() for i in range(5): demo_heuristic_lander(env, seed=1, render=True) env.close()
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CARL-main/examples/try_dm_control.py
# flake8: noqa: F401 # type: ignore import matplotlib.pyplot as plt from carl.envs import CARLDmcFishEnv from carl.envs import CARLDmcFishEnv_defaults as fish_default from carl.envs import CARLDmcFishEnv_mask as fish_mask from carl.envs import CARLDmcQuadrupedEnv from carl.envs import CARLDmcQuadrupedEnv_defaults as quadruped_default from carl.envs import CARLDmcQuadrupedEnv_mask as quadruped_mask from carl.envs import CARLDmcWalkerEnv from carl.envs import CARLDmcWalkerEnv_defaults as walker_default from carl.envs import CARLDmcWalkerEnv_mask as walker_mask if __name__ == "__main__": # Load one task: stronger_act = walker_default.copy() stronger_act["actuator_strength"] = walker_default["actuator_strength"] * 2 contexts = {0: stronger_act} # stronger_act = quadruped_default.copy() # stronger_act["actuator_strength"] = quadruped_default["actuator_strength"]*2 # contexts = {0: stronger_act} # carl_env = CARLDmcQuadrupedEnv(task="fetch_context", contexts=contexts, context_mask=quadruped_mask, hide_context=False) # contexts = {0: fish_default} # carl_env = CARLDmcFishEnv(task="upright_context", contexts=contexts, context_mask=fish_mask, hide_context=False) carl_env = CARLDmcWalkerEnv( task="run_context", contexts=contexts, context_mask=walker_mask, hide_context=False, dict_observation_space=True, ) action = carl_env.action_space.sample() state, reward, done, info = carl_env.step(action=action) print("state", state, type(state)) render = lambda: plt.imshow(carl_env.render(mode="rgb_array")) s = carl_env.reset() render() # plt.savefig("dm_render.png") action = carl_env.action_space.sample() state, reward, done, info = carl_env.step(action=action) print("state", state, type(state)) # s = carl_env.reset() # done = False # i = 0 # while not done: # action = carl_env.action_space.sample() # state, reward, done, info = carl_env.step(action=action) # print(state, action, reward, done) # i += 1 # print(i)
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CARL-main/test/test_CARLEnv.py
from typing import Any, Dict import unittest import numpy as np from carl.envs.classic_control.carl_pendulum import CARLPendulumEnv from carl.utils.types import Context class TestStateConstruction(unittest.TestCase): def test_hiddenstate(self): """ Test if we set hide_context = True that we get the original, normal state. """ env = CARLPendulumEnv( contexts={}, hide_context=True, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, ) env.reset() action = [0.01] # torque state, reward, done, info = env.step(action=action) env.close() self.assertEqual(3, len(state)) def test_visiblestate(self): """ Test if we set hide_context = False and state_context_features=None that we get the normal state extended by all context features. """ env = CARLPendulumEnv( contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, ) env.reset() action = [0.01] # torque state, reward, done, info = env.step(action=action) env.close() self.assertEqual(10, len(state)) def test_visiblestate_customnone(self): """ Test if we set hide_context = False and state_context_features="changing_context_features" that we get the normal state, not extended by context features. """ env = CARLPendulumEnv( contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=["changing_context_features"], ) env.reset() action = [0.01] # torque state, reward, done, info = env.step(action=action) env.close() # Because we don't change any context features the state length should be 3 self.assertEqual(3, len(state)) def test_visiblestate_custom(self): """ Test if we set hide_context = False and state_context_features=["g", "m"] that we get the normal state, extended by the context feature values of g and m. """ env = CARLPendulumEnv( contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=["g", "m"], ) env.reset() action = [0.01] # torque state, reward, done, info = env.step(action=action) env.close() # state should be of length 5 because we add two context features self.assertEqual(5, len(state)) def test_visiblestate_changingcontextfeatures_nochange(self): """ Test if we set hide_context = False and state_context_features="changing_context_features" that we get the normal state, extended by the context features which are changing in the set of contexts. Here: None are changing. """ contexts = { "0": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, "1": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, "2": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, "3": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, } env = CARLPendulumEnv( contexts=contexts, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=["changing_context_features"], ) env.reset() action = [0.01] # torque state, reward, done, info = env.step(action=action) env.close() # state should be of length 3 because all contexts are the same self.assertEqual(3, len(state)) def test_visiblestate_changingcontextfeatures_change(self): """ Test if we set hide_context = False and state_context_features="changing_context_features" that we get the normal state, extended by the context features which are changing in the set of contexts. Here: Two are changing. """ contexts = { "0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.0}, "1": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 0.95}, "2": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 0.3}, "3": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.3}, } env = CARLPendulumEnv( contexts=contexts, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=["changing_context_features"], ) env.reset() action = [0.01] # torque state, reward, done, info = env.step(action=action) env.close() # state should be of length 5 because two features are changing (dt and l) self.assertEqual(5, len(state)) def test_dict_observation_space(self): contexts = {"0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.0}} env = CARLPendulumEnv( contexts=contexts, hide_context=False, dict_observation_space=True, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=["changing_context_features"], ) obs = env.reset() self.assertEqual(type(obs), dict) self.assertTrue("state" in obs) self.assertTrue("context" in obs) action = [0.01] # torque next_obs, reward, done, info = env.step(action=action) env.close() def test_state_context_feature_population(self): env = ( # noqa: F841 local variable is assigned to but never used CARLPendulumEnv( contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="no", ) ) self.assertIsNotNone(env.state_context_features) class TestEpisodeTermination(unittest.TestCase): def test_episode_termination(self): """ Test if we set hide_context = True that we get the original, normal state. """ ep_length = 100 env = CARLPendulumEnv( contexts={}, hide_context=True, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, max_episode_length=ep_length, ) env.reset() action = [0.0] # torque done = False counter = 0 while not done: state, reward, done, info = env.step(action=action) counter += 1 self.assertTrue(counter <= ep_length) if counter > ep_length: break env.close() class TestContextFeatureScaling(unittest.TestCase): def test_context_feature_scaling_no(self): env = ( # noqa: F841 local variable is assigned to but never used CARLPendulumEnv( contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="no", ) ) def test_context_feature_scaling_by_mean(self): contexts = { # order is important because context "0" is checked in the test # because of the reset context "0" must come seond "1": {"max_speed": 16.0, "dt": 0.06, "g": 20.0, "m": 2.0, "l": 3.6}, "0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.8}, } env = CARLPendulumEnv( contexts=contexts, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="by_mean", ) env.reset() action = [0.0] state, reward, done, info = env.step(action=action) n_c = len(env.default_context) scaled_contexts = state[-n_c:] target = np.array( [16 / 12, 0.06 / 0.045, 20 / 15, 2 / 1.5, 3.6 / 2.7, 1, 1] ) # for context "1" self.assertTrue( np.all(target == scaled_contexts), f"target {target} != actual {scaled_contexts}", ) def test_context_feature_scaling_by_default(self): default_context = { "max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0, "initial_angle_max": np.pi, "initial_velocity_max": 1, } contexts = { "0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.8}, } env = CARLPendulumEnv( contexts=contexts, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="by_default", default_context=default_context, ) env.reset() action = [0.0] state, reward, done, info = env.step(action=action) n_c = len(default_context) scaled_contexts = state[-n_c:] self.assertTrue( np.all(np.array([1.0, 0.6, 1, 1, 1.8, 1, 1]) == scaled_contexts) ) def test_context_feature_scaling_by_default_nodefcontext(self): with self.assertRaises(ValueError): env = CARLPendulumEnv( # noqa: F841 local variable is assigned to but never used contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="by_default", default_context=None, ) def test_context_feature_scaling_unknown_init(self): with self.assertRaises(ValueError): env = CARLPendulumEnv( # noqa: F841 local variable is assigned to but never used contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="bork", ) def test_context_feature_scaling_unknown_step(self): env = ( # noqa: F841 local variable is assigned to but never used CARLPendulumEnv( contexts={}, hide_context=False, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="no", ) ) env.reset() env.scale_context_features = "bork" action = [0.01] # torque with self.assertRaises(ValueError): next_obs, reward, done, info = env.step(action=action) def test_context_mask(self): context_mask = ["dt", "g"] env = ( # noqa: F841 local variable is assigned to but never used CARLPendulumEnv( contexts={}, hide_context=False, context_mask=context_mask, dict_observation_space=True, add_gaussian_noise_to_context=False, gaussian_noise_std_percentage=0.01, state_context_features=None, scale_context_features="no", ) ) s = env.reset() s_c = s["context"] forbidden_in_context = [ f for f in env.state_context_features if f in context_mask ] self.assertTrue(len(s_c) == len(list(env.default_context.keys())) - 2) self.assertTrue(len(forbidden_in_context) == 0) class TestContextSelection(unittest.TestCase): @staticmethod def generate_contexts() -> Dict[Any, Context]: keys = "abc" context = {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.8} contexts = {k: context for k in keys} return contexts def test_default_selector(self): from carl.context.selection import RoundRobinSelector contexts = self.generate_contexts() env = CARLPendulumEnv(contexts=contexts) env.reset() self.assertEqual(type(env.context_selector), RoundRobinSelector) self.assertEqual(env.context_selector.n_calls, 1) env.reset() self.assertEqual(env.context_key, "b") def test_roundrobin_selector_init(self): from carl.context.selection import RoundRobinSelector contexts = self.generate_contexts() env = CARLPendulumEnv( contexts=contexts, context_selector=RoundRobinSelector(contexts=contexts) ) self.assertEqual(type(env.context_selector), RoundRobinSelector) def test_random_selector_init(self): from carl.context.selection import RandomSelector contexts = self.generate_contexts() env = CARLPendulumEnv( contexts=contexts, context_selector=RandomSelector(contexts=contexts) ) self.assertEqual(type(env.context_selector), RandomSelector) def test_random_selectorclass_init(self): from carl.context.selection import RandomSelector contexts = self.generate_contexts() env = CARLPendulumEnv(contexts=contexts, context_selector=RandomSelector) self.assertEqual(type(env.context_selector), RandomSelector) def test_unknown_selector_init(self): with self.assertRaises(ValueError): contexts = self.generate_contexts() _ = CARLPendulumEnv(contexts=contexts, context_selector="bork") def test_get_context_key(self): contexts = self.generate_contexts() env = CARLPendulumEnv(contexts=contexts) self.assertEqual(env.context_key, None) class TestContextSampler(unittest.TestCase): def test_get_defaults(self): from carl.context.sampling import get_default_context_and_bounds defaults, bounds = get_default_context_and_bounds(env_name="CARLPendulumEnv") DEFAULT_CONTEXT = { "max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0, "initial_angle_max": np.pi, "initial_velocity_max": 1, } self.assertDictEqual(defaults, DEFAULT_CONTEXT) def test_sample_contexts(self): from carl.context.sampling import sample_contexts contexts = sample_contexts( env_name="CARLPendulumEnv", context_feature_args=["l"], num_contexts=1, default_sample_std_percentage=0.0, ) self.assertEqual(contexts[0]["l"], 1) class TestContextAugmentation(unittest.TestCase): def test_gaussian_noise(self): from carl.context.augmentation import add_gaussian_noise c = add_gaussian_noise(default_value=1, percentage_std=0) self.assertEqual(c, 1) if __name__ == "__main__": unittest.main()
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CARL-main/test/test_all_envs.py
import unittest import numpy as np import carl.envs class TestInitEnvs(unittest.TestCase): def test_init_all_envs(self): global_vars = vars(carl.envs) mustinclude = "CARL" forbidden = ["defaults", "bounds", "mask"] for varname, var in global_vars.items(): if mustinclude in varname and not np.any([f in varname for f in forbidden]): try: env = ( # noqa: F841 local variable is assigned to but never used var() ) except Exception as e: print(f"Cannot instantiate {var} environment.") raise e
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