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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) | |