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| import numpy as np | |
| from gym import utils | |
| from gym.envs.mujoco import MujocoEnv | |
| from gym.spaces import Box | |
| DEFAULT_CAMERA_CONFIG = { | |
| "trackbodyid": 2, | |
| "distance": 4.0, | |
| "lookat": np.array((0.0, 0.0, 1.15)), | |
| "elevation": -20.0, | |
| } | |
| class Walker2dEnv(MujocoEnv, utils.EzPickle): | |
| """ | |
| ### Description | |
| This environment builds on the hopper environment based on the work done by Erez, Tassa, and Todorov | |
| in ["Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks"](http://www.roboticsproceedings.org/rss07/p10.pdf) | |
| by adding another set of legs making it possible for the robot to walker forward instead of | |
| hop. Like other Mujoco environments, this environment aims to increase the number of independent state | |
| and control variables as compared to the classic control environments. The walker is a | |
| two-dimensional two-legged figure that consist of four main body parts - a single torso at the top | |
| (with the two legs splitting after the torso), two thighs in the middle below the torso, two legs | |
| in the bottom below the thighs, and two feet attached to the legs on which the entire body rests. | |
| The goal is to make coordinate both sets of feet, legs, and thighs to move in the forward (right) | |
| direction by applying torques on the six hinges connecting the six body parts. | |
| ### Action Space | |
| The action space is a `Box(-1, 1, (6,), float32)`. An action represents the torques applied at the hinge joints. | |
| | Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit | | |
| |-----|----------------------------------------|-------------|-------------|----------------------------------|-------|--------------| | |
| | 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) | | |
| | 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) | | |
| | 2 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) | | |
| | 3 | Torque applied on the left thigh rotor | -1 | 1 | thigh_left_joint | hinge | torque (N m) | | |
| | 4 | Torque applied on the left leg rotor | -1 | 1 | leg_left_joint | hinge | torque (N m) | | |
| | 5 | Torque applied on the left foot rotor | -1 | 1 | foot_left_joint | hinge | torque (N m) | | |
| ### Observation Space | |
| Observations consist of positional values of different body parts of the walker, | |
| followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities. | |
| By default, observations do not include the x-coordinate of the top. It may | |
| be included by passing `exclude_current_positions_from_observation=False` during construction. | |
| In that case, the observation space will have 18 dimensions where the first dimension | |
| represent the x-coordinates of the top of the walker. | |
| Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x-coordinate | |
| of the top will be returned in `info` with key `"x_position"`. | |
| By default, observation is a `ndarray` with shape `(17,)` where the elements correspond to the following: | |
| | Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit | | |
| | --- | ------------------------------------------------ | ---- | --- | -------------------------------- | ----- | ------------------------ | | |
| | 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz (torso) | slide | position (m) | | |
| | 1 | angle of the top | -Inf | Inf | rooty (torso) | hinge | angle (rad) | | |
| | 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) | | |
| | 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) | | |
| | 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) | | |
| | 5 | angle of the left thigh joint | -Inf | Inf | thigh_left_joint | hinge | angle (rad) | | |
| | 6 | angle of the left leg joint | -Inf | Inf | leg_left_joint | hinge | angle (rad) | | |
| | 7 | angle of the left foot joint | -Inf | Inf | foot_left_joint | hinge | angle (rad) | | |
| | 8 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) | | |
| | 9 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) | | |
| | 10 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) | | |
| | 11 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) | | |
| | 12 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) | | |
| | 13 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) | | |
| | 14 | angular velocity of the thigh hinge | -Inf | Inf | thigh_left_joint | hinge | angular velocity (rad/s) | | |
| | 15 | angular velocity of the leg hinge | -Inf | Inf | leg_left_joint | hinge | angular velocity (rad/s) | | |
| | 16 | angular velocity of the foot hinge | -Inf | Inf | foot_left_joint | hinge | angular velocity (rad/s) | | |
| ### Rewards | |
| The reward consists of three parts: | |
| - *healthy_reward*: Every timestep that the walker is alive, it receives a fixed reward of value `healthy_reward`, | |
| - *forward_reward*: A reward of walking 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 dependeent on the frame_skip parameter | |
| (default is 4), where the frametime is 0.002 - making the default | |
| *dt = 4 * 0.002 = 0.008*. This reward would be positive if the walker walks forward (right) desired. | |
| - *ctrl_cost*: A cost for penalising the walker 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 0.001 | |
| The total reward returned is ***reward*** *=* *healthy_reward bonus + forward_reward - ctrl_cost* and `info` will also contain the individual reward terms | |
| ### Starting State | |
| All observations start in state | |
| (0.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | |
| with a uniform noise in the range of [-`reset_noise_scale`, `reset_noise_scale`] added to the values for stochasticity. | |
| ### Episode End | |
| The walker is said to be unhealthy if any of the following happens: | |
| 1. Any of the state space values is no longer finite | |
| 2. The height of the walker is ***not*** in the closed interval specified by `healthy_z_range` | |
| 3. The absolute value of the angle (`observation[1]` if `exclude_current_positions_from_observation=False`, else `observation[2]`) is ***not*** in the closed interval specified by `healthy_angle_range` | |
| If `terminate_when_unhealthy=True` is passed during construction (which is the default), | |
| the episode ends when any of the following happens: | |
| 1. Truncation: The episode duration reaches a 1000 timesteps | |
| 2. Termination: The walker is unhealthy | |
| If `terminate_when_unhealthy=False` is passed, the episode is ended only when 1000 timesteps are exceeded. | |
| ### Arguments | |
| No additional arguments are currently supported in v2 and lower. | |
| ``` | |
| env = gym.make('Walker2d-v4') | |
| ``` | |
| v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. | |
| ``` | |
| env = gym.make('Walker2d-v4', ctrl_cost_weight=0.1, ....) | |
| ``` | |
| | Parameter | Type | Default | Description | | |
| | -------------------------------------------- | --------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | |
| | `xml_file` | **str** | `"walker2d.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-3` | Weight for _ctr_cost_ term (see section on reward) | | |
| | `healthy_reward` | **float** | `1.0` | Constant reward given if the ant is "healthy" after timestep | | |
| | `terminate_when_unhealthy` | **bool** | `True` | If true, issue a done signal if the z-coordinate of the walker is no longer healthy | | |
| | `healthy_z_range` | **tuple** | `(0.8, 2)` | The z-coordinate of the top of the walker must be in this range to be considered healthy | | |
| | `healthy_angle_range` | **tuple** | `(-1, 1)` | The angle must be in this range to be considered healthy | | |
| | `reset_noise_scale` | **float** | `5e-3` | 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-coordinate 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": 125, | |
| } | |
| def __init__( | |
| self, | |
| forward_reward_weight=1.0, | |
| ctrl_cost_weight=1e-3, | |
| healthy_reward=1.0, | |
| terminate_when_unhealthy=True, | |
| healthy_z_range=(0.8, 2.0), | |
| healthy_angle_range=(-1.0, 1.0), | |
| reset_noise_scale=5e-3, | |
| exclude_current_positions_from_observation=True, | |
| **kwargs | |
| ): | |
| utils.EzPickle.__init__( | |
| self, | |
| forward_reward_weight, | |
| ctrl_cost_weight, | |
| healthy_reward, | |
| terminate_when_unhealthy, | |
| healthy_z_range, | |
| healthy_angle_range, | |
| reset_noise_scale, | |
| exclude_current_positions_from_observation, | |
| **kwargs | |
| ) | |
| self._forward_reward_weight = forward_reward_weight | |
| self._ctrl_cost_weight = ctrl_cost_weight | |
| self._healthy_reward = healthy_reward | |
| self._terminate_when_unhealthy = terminate_when_unhealthy | |
| self._healthy_z_range = healthy_z_range | |
| self._healthy_angle_range = healthy_angle_range | |
| 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=(17,), dtype=np.float64 | |
| ) | |
| else: | |
| observation_space = Box( | |
| low=-np.inf, high=np.inf, shape=(18,), dtype=np.float64 | |
| ) | |
| MujocoEnv.__init__( | |
| self, "walker2d.xml", 4, observation_space=observation_space, **kwargs | |
| ) | |
| def healthy_reward(self): | |
| return ( | |
| float(self.is_healthy or self._terminate_when_unhealthy) | |
| * self._healthy_reward | |
| ) | |
| def control_cost(self, action): | |
| control_cost = self._ctrl_cost_weight * np.sum(np.square(action)) | |
| return control_cost | |
| def is_healthy(self): | |
| z, angle = self.data.qpos[1:3] | |
| min_z, max_z = self._healthy_z_range | |
| min_angle, max_angle = self._healthy_angle_range | |
| healthy_z = min_z < z < max_z | |
| healthy_angle = min_angle < angle < max_angle | |
| is_healthy = healthy_z and healthy_angle | |
| return is_healthy | |
| def terminated(self): | |
| terminated = not self.is_healthy if self._terminate_when_unhealthy else False | |
| return terminated | |
| def _get_obs(self): | |
| position = self.data.qpos.flat.copy() | |
| velocity = np.clip(self.data.qvel.flat.copy(), -10, 10) | |
| if self._exclude_current_positions_from_observation: | |
| position = position[1:] | |
| observation = np.concatenate((position, velocity)).ravel() | |
| return observation | |
| def step(self, action): | |
| x_position_before = self.data.qpos[0] | |
| self.do_simulation(action, self.frame_skip) | |
| x_position_after = self.data.qpos[0] | |
| x_velocity = (x_position_after - x_position_before) / self.dt | |
| ctrl_cost = self.control_cost(action) | |
| forward_reward = self._forward_reward_weight * x_velocity | |
| healthy_reward = self.healthy_reward | |
| rewards = forward_reward + healthy_reward | |
| costs = ctrl_cost | |
| observation = self._get_obs() | |
| reward = rewards - costs | |
| terminated = self.terminated | |
| info = { | |
| "x_position": x_position_after, | |
| "x_velocity": x_velocity, | |
| } | |
| if self.render_mode == "human": | |
| self.render() | |
| return observation, reward, terminated, False, info | |
| 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) | |