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https://github.com/Farama-Foundation/Gymnasium.git
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4487008ea9
* Fixed minor docstring issues found on the website * Updated the top docstrings with mujoco environments that fixed the observation and action tables. Added v4 gym.make code
264 lines
13 KiB
Python
264 lines
13 KiB
Python
import numpy as np
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from gym import utils
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from gym.envs.mujoco import mujoco_env
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DEFAULT_CAMERA_CONFIG = {
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"trackbodyid": 2,
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"distance": 3.0,
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"lookat": np.array((0.0, 0.0, 1.15)),
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"elevation": -20.0,
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}
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class HopperEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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"""
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### Description
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This environment is based on the work done by Erez, Tassa, and Todorov in
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["Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks"](http://www.roboticsproceedings.org/rss07/p10.pdf). The environment aims to
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increase the number of independent state and control variables as compared to
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the classic control environments. The hopper is a two-dimensional
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one-legged figure that consist of four main body parts - the torso at the
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top, the thigh in the middle, the leg in the bottom, and a single foot on
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which the entire body rests. The goal is to make hops that move in the
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forward (right) direction by applying torques on the three hinges
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connecting the four body parts.
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### Action Space
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The agent take a 3-element vector for actions.
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The action space is a continuous `(action, action, action)` all in `[-1, 1]`
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, where `action` represents the numerical torques applied between *links*
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| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
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|-----|------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
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| 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) |
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| 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) |
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| 3 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) |
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### Observation Space
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The state space consists of positional values of different body parts of the
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hopper, followed by the velocities of those individual parts
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(their derivatives) with all the positions ordered before all the velocities.
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The observation is a `ndarray` with shape `(11,)` where the elements
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correspond to the following:
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| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
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|-----|--------------------------------------------------|------|-----|----------------------------------|-------|--------------------------|
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| 0 | x-coordinate of the top | -Inf | Inf | rootx | slide | position (m) |
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| 1 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz | slide | position (m) |
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| 2 | angle of the top | -Inf | Inf | rooty | hinge | angle (rad) |
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| 3 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
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| 4 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
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| 5 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
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| 6 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
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| 7 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
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| 8 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
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| 9 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
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| 10 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
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| 11 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
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**Note:**
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In practice (and Gym implementation), the first positional element is
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omitted from the state space since the reward function is calculated based
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on that value. This value is hidden from the algorithm, which in turn has
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to develop an abstract understanding of it from the observed rewards.
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Therefore, observation space has shape `(11,)` instead of `(12,)` and looks like:
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| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
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|-----|--------------------------------------------------|------|-----|----------------------------------|-------|--------------------------|
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| 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz | slide | position (m) |
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| 1 | angle of the top | -Inf | Inf | rooty | hinge | angle (rad) |
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| 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
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| 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
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| 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
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| 5 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
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| 6 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
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| 7 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
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| 8 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
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| 9 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
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| 10 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
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### Rewards
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The reward consists of three parts:
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- *alive bonus*: Every timestep that the hopper is alive, it gets a reward of 1,
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- *reward_forward*: A reward of hopping forward which is measured
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as *(x-coordinate before action - x-coordinate after action)/dt*. *dt* is
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the time between actions and is dependent on the frame_skip parameter
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(default is 4), where the *dt* for one frame is 0.002 - making the
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default *dt = 4*0.002 = 0.008*. This reward would be positive if the hopper
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hops forward (right) desired.
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- *reward_control*: A negative reward for penalising the hopper if it takes
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actions that are too large. It is measured as *-coefficient **x**
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sum(action<sup>2</sup>)* where *coefficient* is a parameter set for the
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control and has a default value of 0.001
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The total reward returned is ***reward*** *=* *alive bonus + reward_forward + reward_control*
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### Starting State
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All observations start in state
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(0.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) with a uniform noise
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in the range of [-0.005, 0.005] added to the values for stochasticity.
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### Episode Termination
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The episode terminates when any of the following happens:
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1. The episode duration reaches a 1000 timesteps
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2. Any of the state space values is no longer finite
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3. The absolute value of any of the state variable indexed (angle and beyond) is greater than 100
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4. The height of the hopper becomes greater than 0.7 metres (hopper has hopped too high).
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5. The absolute value of the angle (index 2) is less than 0.2 radians (hopper has fallen down).
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### Arguments
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No additional arguments are currently supported (in v2 and lower), but
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modifications can be made to the XML file in the assets folder
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(or by changing the path to a modified XML file in another folder).
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```
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env = gym.make('Hopper-v2')
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```
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v3 and v4 take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
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```
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env = gym.make('Hopper-v4', ctrl_cost_weight=0.1, ....)
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```
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### Version History
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* v4: all mujoco environments now use the mujoco bindings in mujoco>=2.1.3
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* 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)
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* v2: All continuous control environments now use mujoco_py >= 1.50
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* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
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* v0: Initial versions release (1.0.0)
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"""
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def __init__(
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self,
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xml_file="hopper.xml",
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forward_reward_weight=1.0,
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ctrl_cost_weight=1e-3,
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healthy_reward=1.0,
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terminate_when_unhealthy=True,
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healthy_state_range=(-100.0, 100.0),
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healthy_z_range=(0.7, float("inf")),
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healthy_angle_range=(-0.2, 0.2),
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reset_noise_scale=5e-3,
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exclude_current_positions_from_observation=True,
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):
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utils.EzPickle.__init__(**locals())
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self._forward_reward_weight = forward_reward_weight
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self._ctrl_cost_weight = ctrl_cost_weight
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self._healthy_reward = healthy_reward
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self._terminate_when_unhealthy = terminate_when_unhealthy
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self._healthy_state_range = healthy_state_range
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self._healthy_z_range = healthy_z_range
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self._healthy_angle_range = healthy_angle_range
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self._reset_noise_scale = reset_noise_scale
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self._exclude_current_positions_from_observation = (
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exclude_current_positions_from_observation
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)
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mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
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@property
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def healthy_reward(self):
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return (
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float(self.is_healthy or self._terminate_when_unhealthy)
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* self._healthy_reward
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)
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def control_cost(self, action):
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control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
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return control_cost
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@property
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def is_healthy(self):
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z, angle = self.data.qpos[1:3]
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state = self.state_vector()[2:]
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min_state, max_state = self._healthy_state_range
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min_z, max_z = self._healthy_z_range
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min_angle, max_angle = self._healthy_angle_range
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healthy_state = np.all(np.logical_and(min_state < state, state < max_state))
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healthy_z = min_z < z < max_z
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healthy_angle = min_angle < angle < max_angle
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is_healthy = all((healthy_state, healthy_z, healthy_angle))
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return is_healthy
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@property
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def done(self):
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done = not self.is_healthy if self._terminate_when_unhealthy else False
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return done
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def _get_obs(self):
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position = self.data.qpos.flat.copy()
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velocity = np.clip(self.data.qvel.flat.copy(), -10, 10)
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if self._exclude_current_positions_from_observation:
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position = position[1:]
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observation = np.concatenate((position, velocity)).ravel()
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return observation
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def step(self, action):
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x_position_before = self.data.qpos[0]
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self.do_simulation(action, self.frame_skip)
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x_position_after = self.data.qpos[0]
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x_velocity = (x_position_after - x_position_before) / self.dt
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ctrl_cost = self.control_cost(action)
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forward_reward = self._forward_reward_weight * x_velocity
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healthy_reward = self.healthy_reward
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rewards = forward_reward + healthy_reward
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costs = ctrl_cost
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observation = self._get_obs()
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reward = rewards - costs
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done = self.done
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info = {
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"x_position": x_position_after,
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"x_velocity": x_velocity,
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}
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return observation, reward, done, info
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def reset_model(self):
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noise_low = -self._reset_noise_scale
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noise_high = self._reset_noise_scale
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qpos = self.init_qpos + self.np_random.uniform(
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low=noise_low, high=noise_high, size=self.model.nq
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)
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qvel = self.init_qvel + self.np_random.uniform(
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low=noise_low, high=noise_high, size=self.model.nv
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)
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self.set_state(qpos, qvel)
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observation = self._get_obs()
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return observation
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def viewer_setup(self):
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for key, value in DEFAULT_CAMERA_CONFIG.items():
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if isinstance(value, np.ndarray):
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getattr(self.viewer.cam, key)[:] = value
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else:
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setattr(self.viewer.cam, key, value)
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