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Merge pull request #49 from jjshoots/pole_env
Add pole balancing environments
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,189 @@ | ||
| """QuadX Pole Balance Environment.""" | ||
| from __future__ import annotations | ||
|
|
||
| from typing import Any, Literal | ||
|
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| import numpy as np | ||
| from gymnasium import spaces | ||
|
|
||
| from PyFlyt.gym_envs.quadx_envs.quadx_base_env import QuadXBaseEnv | ||
| from PyFlyt.gym_envs.utils.pole_handler import PoleHandler | ||
|
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| class QuadXPoleBalanceEnv(QuadXBaseEnv): | ||
| """Simple Hover Environment with the additional goal of keeping a pole upright. | ||
| Actions are vp, vq, vr, T, ie: angular rates and thrust. | ||
| The target is to not crash and not let the pole hit the ground for the longest time possible. | ||
| Args: | ||
| ---- | ||
| sparse_reward (bool): whether to use sparse rewards or not. | ||
| flight_mode (int): the flight mode of the UAV | ||
| flight_dome_size (float): size of the allowable flying area. | ||
| max_duration_seconds (float): maximum simulation time of the environment. | ||
| angle_representation (Literal["euler", "quaternion"]): can be "euler" or "quaternion". | ||
| agent_hz (int): looprate of the agent to environment interaction. | ||
| render_mode (None | Literal["human", "rgb_array"]): render_mode | ||
| render_resolution (tuple[int, int]): render_resolution. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| sparse_reward: bool = False, | ||
| flight_mode: int = 0, | ||
| flight_dome_size: float = 3.0, | ||
| max_duration_seconds: float = 20.0, | ||
| angle_representation: Literal["euler", "quaternion"] = "quaternion", | ||
| agent_hz: int = 40, | ||
| render_mode: None | Literal["human", "rgb_array"] = None, | ||
| render_resolution: tuple[int, int] = (480, 480), | ||
| ): | ||
| """__init__. | ||
| Args: | ||
| ---- | ||
| sparse_reward (bool): whether to use sparse rewards or not. | ||
| flight_mode (int): the flight mode of the UAV | ||
| flight_dome_size (float): size of the allowable flying area. | ||
| max_duration_seconds (float): maximum simulation time of the environment. | ||
| angle_representation (Literal["euler", "quaternion"]): can be "euler" or "quaternion". | ||
| agent_hz (int): looprate of the agent to environment interaction. | ||
| render_mode (None | Literal["human", "rgb_array"]): render_mode | ||
| render_resolution (tuple[int, int]): render_resolution. | ||
| """ | ||
| super().__init__( | ||
| flight_mode=flight_mode, | ||
| flight_dome_size=flight_dome_size, | ||
| max_duration_seconds=max_duration_seconds, | ||
| angle_representation=angle_representation, | ||
| agent_hz=agent_hz, | ||
| render_mode=render_mode, | ||
| render_resolution=render_resolution, | ||
| ) | ||
| # init the pole | ||
| self.pole = PoleHandler() | ||
|
|
||
| """GYMNASIUM STUFF""" | ||
| # Define observation space | ||
| self.observation_space = spaces.Box( | ||
| low=-np.inf, | ||
| high=np.inf, | ||
| shape=( | ||
| self.combined_space.shape[0] + self.pole.observation_space.shape[0], | ||
| ), | ||
| dtype=np.float64, | ||
| ) | ||
|
|
||
| """ ENVIRONMENT CONSTANTS """ | ||
| self.sparse_reward = sparse_reward | ||
|
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||
| def reset( | ||
| self, *, seed: None | int = None, options: None | dict[str, Any] = dict() | ||
| ) -> tuple[np.ndarray, dict[str, Any]]: | ||
| """reset. | ||
| Args: | ||
| ---- | ||
| seed: seed to pass to the base environment. | ||
| options: None | ||
| """ | ||
| super().begin_reset( | ||
| seed, | ||
| options, | ||
| drone_options={"drone_model": "primitive_drone"}, | ||
| ) | ||
| self.pole.reset(p=self.env, start_location=np.array([0.0, 0.0, 1.55])) | ||
| super().end_reset(seed, options) | ||
|
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| return self.state, self.info | ||
|
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| def compute_state(self) -> None: | ||
| """Computes the state of the current timestep. | ||
| This returns the observation. | ||
| - ang_vel (vector of 3 values) | ||
| - ang_pos (vector of 3/4 values) | ||
| - lin_vel (vector of 3 values) | ||
| - lin_pos (vector of 3 values) | ||
| - previous_action (vector of 4 values) | ||
| - auxiliary information (vector of 4 values) | ||
| - 12 values for the pole's positions relative to self: | ||
| ------ top position XYZ | ||
| ------ bottom position XYZ | ||
| ------ top velocity XYZ | ||
| ------ bottom velocity XYZ | ||
| - auxiliary information (vector of 4 values) | ||
| """ | ||
| # compute attitude of self | ||
| ang_vel, ang_pos, lin_vel, lin_pos, quaternion = super().compute_attitude() | ||
| aux_state = super().compute_auxiliary() | ||
| rotation = ( | ||
| np.array(self.env.getMatrixFromQuaternion(quaternion)).reshape(3, 3).T | ||
| ) | ||
|
|
||
| # compute the pole's states | ||
| ( | ||
| pole_top_pos, | ||
| pole_top_vel, | ||
| pole_bot_pos, | ||
| pole_bot_vel, | ||
| ) = self.pole.compute_state( | ||
| rotation=rotation, | ||
| uav_lin_pos=lin_pos, | ||
| uav_lin_vel=lin_vel, | ||
| ) | ||
|
|
||
| # combine everything | ||
| if self.angle_representation == 0: | ||
| self.state = np.concatenate( | ||
| [ | ||
| ang_vel, | ||
| ang_pos, | ||
| lin_vel, | ||
| lin_pos, | ||
| self.action, | ||
| aux_state, | ||
| pole_top_pos, | ||
| pole_bot_pos, | ||
| pole_top_vel, | ||
| pole_bot_vel, | ||
| ], | ||
| axis=-1, | ||
| ) | ||
| elif self.angle_representation == 1: | ||
| self.state = np.concatenate( | ||
| [ | ||
| ang_vel, | ||
| quaternion, | ||
| lin_vel, | ||
| lin_pos, | ||
| self.action, | ||
| aux_state, | ||
| pole_top_pos, | ||
| pole_bot_pos, | ||
| pole_top_vel, | ||
| pole_bot_vel, | ||
| ], | ||
| axis=-1, | ||
| ) | ||
|
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| def compute_term_trunc_reward(self) -> None: | ||
| """Computes the termination, truncation, and reward of the current timestep.""" | ||
| super().compute_base_term_trunc_reward() | ||
|
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||
| if not self.sparse_reward: | ||
| # distance from 0, 0, 1 hover point | ||
| linear_distance = np.linalg.norm( | ||
| self.env.state(0)[-1] - np.array([0.0, 0.0, 1.0]) | ||
| ) | ||
|
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| # how far are we from 0 roll pitch | ||
| angular_distance = np.linalg.norm(self.env.state(0)[1][:2]) | ||
|
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| self.reward -= linear_distance + angular_distance | ||
| self.reward -= self.pole.leaningness | ||
| self.reward += 1.0 |
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