299 lines
16 KiB
Python
299 lines
16 KiB
Python
import numpy as np
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from gymnasium import utils
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from gymnasium.envs.mujoco import MujocoEnv
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from gymnasium.spaces import Box
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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(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 action space is a `Box(-1, 1, (3,), float32)`. An action represents the torques applied at the hinge joints.
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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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| 2 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) |
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## Observation Space
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Observations consist 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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By default, observations do not include the x-coordinate of the hopper. It may
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be included by passing `exclude_current_positions_from_observation=False` during construction.
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In that case, the observation space will be `Box(-Inf, Inf, (12,), float64)` where the first observation
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represents the x-coordinate of the hopper.
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Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x-coordinate
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will be returned in `info` with key `"x_position"`.
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However, by default, the observation is a `Box(-Inf, Inf, (11,), float64)` 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 | z-coordinate of the torso (height of hopper) | -Inf | Inf | rootz | slide | position (m) |
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| 1 | angle of the torso | -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 torso | -Inf | Inf | rootx | slide | velocity (m/s) |
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| 6 | velocity of the z-coordinate (height) of the torso | -Inf | Inf | rootz | slide | velocity (m/s) |
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| 7 | angular velocity of the angle of the torso | -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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| excluded | x-coordinate of the torso | -Inf | Inf | rootx | slide | position (m) |
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## Rewards
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The reward consists of three parts:
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- *healthy_reward*: Every timestep that the hopper is healthy (see definition in section "Episode Termination"), it gets a reward of fixed value `healthy_reward`.
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- *forward_reward*: A reward of hopping forward which is measured
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as *`forward_reward_weight` * (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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(fixed to 4), where the frametime 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 (positive x direction).
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- *ctrl_cost*: A cost for penalising the hopper if it takes
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actions that are too large. It is measured as *`ctrl_cost_weight` *
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sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* 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*** *=* *healthy_reward + forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
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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 [-`reset_noise_scale`, `reset_noise_scale`] added to the values for stochasticity.
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## Episode End
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The hopper is said to be unhealthy if any of the following happens:
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1. An element of `observation[1:]` (if `exclude_current_positions_from_observation=True`, else `observation[2:]`) is no longer contained in the closed interval specified by the argument `healthy_state_range`
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2. The height of the hopper (`observation[0]` if `exclude_current_positions_from_observation=True`, else `observation[1]`) is no longer contained in the closed interval specified by the argument `healthy_z_range` (usually meaning that it has fallen)
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3. The angle (`observation[1]` if `exclude_current_positions_from_observation=True`, else `observation[2]`) is no longer contained in the closed interval specified by the argument `healthy_angle_range`
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If `terminate_when_unhealthy=True` is passed during construction (which is the default),
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the episode ends when any of the following happens:
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1. Truncation: The episode duration reaches a 1000 timesteps
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2. Termination: The hopper is unhealthy
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If `terminate_when_unhealthy=False` is passed, the episode is ended only when 1000 timesteps are exceeded.
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## Arguments
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No additional arguments are currently supported in v2 and lower.
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```python
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import gymnasium as gym
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env = gym.make('Hopper-v2')
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```
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v3 and v4 take `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc.
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```python
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import gymnasium as gym
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env = gym.make('Hopper-v4', ctrl_cost_weight=0.1, ....)
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```
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| Parameter | Type | Default | Description |
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| -------------------------------------------- | --------- | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `xml_file` | **str** | `"hopper.xml"` | Path to a MuJoCo model |
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| `forward_reward_weight` | **float** | `1.0` | Weight for _forward_reward_ term (see section on reward) |
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| `ctrl_cost_weight` | **float** | `0.001` | Weight for _ctrl_cost_ reward (see section on reward) |
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| `healthy_reward` | **float** | `1` | Constant reward given if the ant is "healthy" after timestep |
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| `terminate_when_unhealthy` | **bool** | `True` | If true, issue a done signal if the hopper is no longer healthy |
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| `healthy_state_range` | **tuple** | `(-100, 100)` | The elements of `observation[1:]` (if `exclude_current_positions_from_observation=True`, else `observation[2:]`) must be in this range for the hopper to be considered healthy |
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| `healthy_z_range` | **tuple** | `(0.7, float("inf"))` | The z-coordinate must be in this range for the hopper to be considered healthy |
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| `healthy_angle_range` | **tuple** | `(-0.2, 0.2)` | The angle given by `observation[1]` (if `exclude_current_positions_from_observation=True`, else `observation[2]`) must be in this range for the hopper to be considered healthy |
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| `reset_noise_scale` | **float** | `5e-3` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
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| `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 |
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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 `gymnasium.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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metadata = {
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"render_modes": [
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"human",
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"rgb_array",
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"depth_array",
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],
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"render_fps": 125,
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}
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def __init__(
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self,
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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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**kwargs,
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):
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utils.EzPickle.__init__(
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self,
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forward_reward_weight,
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ctrl_cost_weight,
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healthy_reward,
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terminate_when_unhealthy,
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healthy_state_range,
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healthy_z_range,
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healthy_angle_range,
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reset_noise_scale,
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exclude_current_positions_from_observation,
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**kwargs,
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)
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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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if exclude_current_positions_from_observation:
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(11,), dtype=np.float64
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)
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else:
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(12,), dtype=np.float64
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)
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MujocoEnv.__init__(
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self,
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"hopper.xml",
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4,
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observation_space=observation_space,
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default_camera_config=DEFAULT_CAMERA_CONFIG,
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**kwargs,
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)
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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 terminated(self):
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terminated = not self.is_healthy if self._terminate_when_unhealthy else False
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return terminated
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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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terminated = self.terminated
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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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if self.render_mode == "human":
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self.render()
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return observation, reward, terminated, False, 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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