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__credits__ = ["Rushiv Arora"]
import numpy as np
from gym import utils
from gym.envs.mujoco import MujocoEnv
from gym.spaces import Box
DEFAULT_CAMERA_CONFIG = {}
class SwimmerEnv(MujocoEnv, utils.EzPickle):
"""
### Description
This environment corresponds to the Swimmer environment described in Rémi Coulom's PhD thesis
["Reinforcement Learning Using Neural Networks, with Applications to Motor Control"](https://tel.archives-ouvertes.fr/tel-00003985/document).
The environment aims to increase the number of independent state and control
variables as compared to the classic control environments. The swimmers
consist of three or more segments ('***links***') and one less articulation
joints ('***rotors***') - one rotor joint connecting exactly two links to
form a linear chain. The swimmer is suspended in a two dimensional pool and
always starts in the same position (subject to some deviation drawn from an
uniform distribution), and the goal is to move as fast as possible towards
the right by applying torque on the rotors and using the fluids friction.
### Notes
The problem parameters are:
Problem parameters:
* *n*: number of body parts
* *m<sub>i</sub>*: mass of part *i* (*i* ∈ {1...n})
* *l<sub>i</sub>*: length of part *i* (*i* ∈ {1...n})
* *k*: viscous-friction coefficient
While the default environment has *n* = 3, *l<sub>i</sub>* = 0.1,
and *k* = 0.1. It is possible to pass a custom MuJoCo XML file during construction to increase the
number of links, or to tweak any of the parameters.
### Action Space
The action space is a `Box(-1, 1, (2,), float32)`. An action represents the torques applied between *links*
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Torque applied on the first rotor | -1 | 1 | motor1_rot | hinge | torque (N m) |
| 1 | Torque applied on the second rotor | -1 | 1 | motor2_rot | hinge | torque (N m) |
### Observation Space
By default, observations consists of:
* θ<sub>i</sub>: angle of part *i* with respect to the *x* axis
* θ<sub>i</sub>': its derivative with respect to time (angular velocity)
In the default case, observations do not include the x- and y-coordinates of the front tip. These may
be included by passing `exclude_current_positions_from_observation=False` during construction.
Then, the observation space will have 10 dimensions where the first two dimensions
represent the x- and y-coordinates of the front tip.
Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x- and y-coordinates
will be returned in `info` with keys `"x_position"` and `"y_position"`, respectively.
By default, the observation is a `ndarray` with shape `(8,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
| --- | ------------------------------------ | ---- | --- | -------------------------------- | ----- | ------------------------ |
| 0 | angle of the front tip | -Inf | Inf | free_body_rot | hinge | angle (rad) |
| 1 | angle of the first rotor | -Inf | Inf | motor1_rot | hinge | angle (rad) |
| 2 | angle of the second rotor | -Inf | Inf | motor2_rot | hinge | angle (rad) |
| 3 | velocity of the tip along the x-axis | -Inf | Inf | slider1 | slide | velocity (m/s) |
| 4 | velocity of the tip along the y-axis | -Inf | Inf | slider2 | slide | velocity (m/s) |
| 5 | angular velocity of front tip | -Inf | Inf | free_body_rot | hinge | angular velocity (rad/s) |
| 6 | angular velocity of first rotor | -Inf | Inf | motor1_rot | hinge | angular velocity (rad/s) |
| 7 | angular velocity of second rotor | -Inf | Inf | motor2_rot | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of two parts:
- *forward_reward*: A reward of moving forward which is measured
as *`forward_reward_weight` * (x-coordinate before action - x-coordinate after action)/dt*. *dt* is
the time between actions and is dependent on the frame_skip parameter
(default is 4), where the frametime is 0.01 - making the
default *dt = 4 * 0.01 = 0.04*. This reward would be positive if the swimmer
swims right as desired.
- *ctrl_cost*: A cost for penalising the swimmer if it takes
actions that are too large. It is measured as *`ctrl_cost_weight` *
sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* is a parameter set for the
control and has a default value of 1e-4
The total reward returned is ***reward*** *=* *forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
### Starting State
All observations start in state (0,0,0,0,0,0,0,0) with a Uniform noise in the range of [-`reset_noise_scale`, `reset_noise_scale`] is added to the initial state for stochasticity.
### Episode End
The episode truncates when the episode length is greater than 1000.
### Arguments
No additional arguments are currently supported in v2 and lower.
```
gym.make('Swimmer-v4')
```
v3 and v4 take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Swimmer-v4', ctrl_cost_weight=0.1, ....)
```
| Parameter | Type | Default | Description |
| -------------------------------------------- | --------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `xml_file` | **str** | `"swimmer.xml"` | Path to a MuJoCo model |
| `forward_reward_weight` | **float** | `1.0` | Weight for _forward_reward_ term (see section on reward) |
| `ctrl_cost_weight` | **float** | `1e-4` | Weight for _ctrl_cost_ term (see section on reward) |
| `reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
| `exclude_current_positions_from_observation` | **bool** | `True` | Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
### Version History
* v4: all mujoco environments now use the mujoco bindings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps": 25,
}
def __init__(
self,
forward_reward_weight=1.0,
ctrl_cost_weight=1e-4,
reset_noise_scale=0.1,
exclude_current_positions_from_observation=True,
**kwargs
):
utils.EzPickle.__init__(
self,
forward_reward_weight,
ctrl_cost_weight,
reset_noise_scale,
exclude_current_positions_from_observation,
**kwargs
)
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
if exclude_current_positions_from_observation:
observation_space = Box(
low=-np.inf, high=np.inf, shape=(8,), dtype=np.float64
)
else:
observation_space = Box(
low=-np.inf, high=np.inf, shape=(10,), dtype=np.float64
)
MujocoEnv.__init__(
self, "swimmer.xml", 4, observation_space=observation_space, **kwargs
)
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
return control_cost
def step(self, action):
xy_position_before = self.data.qpos[0:2].copy()
self.do_simulation(action, self.frame_skip)
xy_position_after = self.data.qpos[0:2].copy()
xy_velocity = (xy_position_after - xy_position_before) / self.dt
x_velocity, y_velocity = xy_velocity
forward_reward = self._forward_reward_weight * x_velocity
ctrl_cost = self.control_cost(action)
observation = self._get_obs()
reward = forward_reward - ctrl_cost
info = {
"reward_fwd": forward_reward,
"reward_ctrl": -ctrl_cost,
"x_position": xy_position_after[0],
"y_position": xy_position_after[1],
"distance_from_origin": np.linalg.norm(xy_position_after, ord=2),
"x_velocity": x_velocity,
"y_velocity": y_velocity,
"forward_reward": forward_reward,
}
if self.render_mode == "human":
self.render()
return observation, reward, False, False, info
def _get_obs(self):
position = self.data.qpos.flat.copy()
velocity = self.data.qvel.flat.copy()
if self._exclude_current_positions_from_observation:
position = position[2:]
observation = np.concatenate([position, velocity]).ravel()
return observation
def reset_model(self):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nq
)
qvel = self.init_qvel + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nv
)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
assert self.viewer is not None
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)
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