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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 = { | |
| "distance": 4.0, | |
| } | |
| class HalfCheetahEnv(MujocoEnv, utils.EzPickle): | |
| """ | |
| ### Description | |
| This environment is based on the work by P. Wawrzyński in | |
| ["A Cat-Like Robot Real-Time Learning to Run"](http://staff.elka.pw.edu.pl/~pwawrzyn/pub-s/0812_LSCLRR.pdf). | |
| The HalfCheetah is a 2-dimensional robot consisting of 9 links and 8 | |
| joints connecting them (including two paws). The goal is to apply a torque | |
| on the joints to make the cheetah run forward (right) as fast as possible, | |
| with a positive reward allocated based on the distance moved forward and a | |
| negative reward allocated for moving backward. The torso and head of the | |
| cheetah are fixed, and the torque can only be applied on the other 6 joints | |
| over the front and back thighs (connecting to the torso), shins | |
| (connecting to the thighs) and feet (connecting to the shins). | |
| ### Action Space | |
| The action space is a `Box(-1, 1, (6,), 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 back thigh rotor | -1 | 1 | bthigh | hinge | torque (N m) | | |
| | 1 | Torque applied on the back shin rotor | -1 | 1 | bshin | hinge | torque (N m) | | |
| | 2 | Torque applied on the back foot rotor | -1 | 1 | bfoot | hinge | torque (N m) | | |
| | 3 | Torque applied on the front thigh rotor | -1 | 1 | fthigh | hinge | torque (N m) | | |
| | 4 | Torque applied on the front shin rotor | -1 | 1 | fshin | hinge | torque (N m) | | |
| | 5 | Torque applied on the front foot rotor | -1 | 1 | ffoot | hinge | torque (N m) | | |
| ### Observation Space | |
| Observations consist of positional values of different body parts of the | |
| cheetah, 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 cheetah's center of mass. 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 | |
| represents the x-coordinate of the cheetah's center of mass. | |
| Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x-coordinate | |
| will be returned in `info` with key `"x_position"`. | |
| However, by default, the 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 front tip | -Inf | Inf | rootz | slide | position (m) | | |
| | 1 | angle of the front tip | -Inf | Inf | rooty | hinge | angle (rad) | | |
| | 2 | angle of the second rotor | -Inf | Inf | bthigh | hinge | angle (rad) | | |
| | 3 | angle of the second rotor | -Inf | Inf | bshin | hinge | angle (rad) | | |
| | 4 | velocity of the tip along the x-axis | -Inf | Inf | bfoot | hinge | angle (rad) | | |
| | 5 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge | angle (rad) | | |
| | 6 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angle (rad) | | |
| | 7 | angular velocity of second rotor | -Inf | Inf | ffoot | hinge | angle (rad) | | |
| | 8 | x-coordinate of the front tip | -Inf | Inf | rootx | slide | velocity (m/s) | | |
| | 9 | y-coordinate of the front tip | -Inf | Inf | rootz | slide | velocity (m/s) | | |
| | 10 | angle of the front tip | -Inf | Inf | rooty | hinge | angular velocity (rad/s) | | |
| | 11 | angle of the second rotor | -Inf | Inf | bthigh | hinge | angular velocity (rad/s) | | |
| | 12 | angle of the second rotor | -Inf | Inf | bshin | hinge | angular velocity (rad/s) | | |
| | 13 | velocity of the tip along the x-axis | -Inf | Inf | bfoot | hinge | angular velocity (rad/s) | | |
| | 14 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge | angular velocity (rad/s) | | |
| | 15 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angular velocity (rad/s) | | |
| | 16 | angular velocity of second rotor | -Inf | Inf | ffoot | 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 | |
| (fixed to 5), where the frametime is 0.01 - making the | |
| default *dt = 5 * 0.01 = 0.05*. This reward would be positive if the cheetah | |
| runs forward (right). | |
| - *ctrl_cost*: A cost for penalising the cheetah 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 0.1 | |
| 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, 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 noise added to the | |
| initial state for stochasticity. As seen before, the first 8 values in the | |
| state are positional and the last 9 values are velocity. A uniform noise in | |
| the range of [-`reset_noise_scale`, `reset_noise_scale`] is added to the positional values while a standard | |
| normal noise with a mean of 0 and standard deviation of `reset_noise_scale` is added to the | |
| initial velocity values of all zeros. | |
| ### Episode End | |
| The episode truncates when the episode length is greater than 1000. | |
| ### Arguments | |
| No additional arguments are currently supported in v2 and lower. | |
| ``` | |
| env = gym.make('HalfCheetah-v2') | |
| ``` | |
| v3 and v4 take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. | |
| ``` | |
| env = gym.make('HalfCheetah-v4', ctrl_cost_weight=0.1, ....) | |
| ``` | |
| | Parameter | Type | Default | Description | | |
| | -------------------------------------------- | --------- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | |
| | `xml_file` | **str** | `"half_cheetah.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** | `0.1` | Weight for _ctrl_cost_ weight (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-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": 20, | |
| } | |
| def __init__( | |
| self, | |
| forward_reward_weight=1.0, | |
| ctrl_cost_weight=0.1, | |
| 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=(17,), dtype=np.float64 | |
| ) | |
| else: | |
| observation_space = Box( | |
| low=-np.inf, high=np.inf, shape=(18,), dtype=np.float64 | |
| ) | |
| MujocoEnv.__init__( | |
| self, "half_cheetah.xml", 5, 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): | |
| 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 | |
| observation = self._get_obs() | |
| reward = forward_reward - ctrl_cost | |
| terminated = False | |
| info = { | |
| "x_position": x_position_after, | |
| "x_velocity": x_velocity, | |
| "reward_run": forward_reward, | |
| "reward_ctrl": -ctrl_cost, | |
| } | |
| if self.render_mode == "human": | |
| self.render() | |
| return observation, reward, terminated, 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[1:] | |
| 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._reset_noise_scale * self.np_random.standard_normal(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) | |