README.md

mujoco-maze

Some maze environments for reinforcement learning (RL) based on mujoco-py and openai gym.

Thankfully, this project is based on the code from rllab and tensorflow/models.

Note that d4rl and dm_control have similar maze environments, and you can also check them. But, if you want more customizable or minimal one, I recommend this.

Usage

Importing mujoco_maze registers environments and you can load environments by gym.make. All available environments listed are listed in [Environments] section.

E.g.,:

import gym
import mujoco_maze  # noqa
env = gym.make("Ant4Rooms-v0")

Environments

• PointUMaze/AntUmaze/SwimmerUmaze

  

  • PointUMaze-v0/AntUMaze-v0/SwimmerUMaze-v0 (Distance-based Reward)
  • PointUmaze-v1/AntUMaze-v1/SwimmerUMaze-v (Goal-based Reward i.e., 1.0 or -ε)

• PointSquareRoom/AntSquareRoom/SwimmerSquareRoom

  

  • PointSquareRoom-v0/AntSquareRoom-v0/SwimmerSquareRoom-v0 (Distance-based Reward)
  • PointSquareRoom-v1/AntSquareRoom-v1/SwimmerSquareRoom-v1 (Goal-based Reward)
  • PointSquareRoom-v2/AntSquareRoom-v2/SwimmerSquareRoom-v2 (No Reward)

• Point4Rooms/Ant4Rooms/Swimmer4Rooms

  

  • Point4Rooms-v0/Ant4Rooms-v0/Swimmer4Rooms-v0 (Distance-based Reward)
  • Point4Rooms-v1/Ant4Rooms-v1/Swimmer4Rooms-v1 (Goal-based Reward)
  • Point4Rooms-v2/Ant4Rooms-v2/Swimmer4Rooms-v2 (Multiple Goals (0.5 pt or 1.0 pt))

• PointCorridor/AntCorridor/SwimmerCorridor

  

  • PointCorridor-v0/AntCorridor-v0/SwimmerCorridor-v0 (Distance-based Reward)
  • PointCorridor-v1/AntCorridor-v1/SwimmerCorridor-v1 (Goal-based Reward)
  • PointCorridor-v2/AntCorridor-v2/SwimmerCorridor-v2 (No Reward)

• PointPush/AntPush

  

  • PointPush-v0/AntPush-v0 (Distance-based Reward)
  • PointPush-v1/AntPush-v1 (Goal-based Reward)

• PointFall/AntFall

  

  • PointFall-v0/AntFall-v0 (Distance-based Reward)
  • PointFall-v1/AntFall-v1 (Goal-based Reward)

• PointBilliard

  

  • PointBilliard-v0 (Distance-based Reward)
  • PointBilliard-v1 (Goal-based Reward)
  • PointBilliard-v2 (Multiple Goals (0.5 pt or 1.0 pt))
  • PointBilliard-v3 (Two goals (0.5 pt or 1.0 pt))
  • PointBilliard-v4 (No Reward)

Customize Environments

You can define your own task by using components in maze_task.py, like:

import gym
import numpy as np
from mujoco_maze.maze_env_utils import MazeCell
from mujoco_maze.maze_task import MazeGoal, MazeTask
from mujoco_maze.point import PointEnv


class GoalRewardEMaze(MazeTask):
    REWARD_THRESHOLD: float = 0.9
    PENALTY: float = -0.0001

    def __init__(self, scale):
        super().__init__(scale)
        self.goals = [MazeGoal(np.array([0.0, 4.0]) * scale)]

    def reward(self, obs):
        return 1.0 if self.termination(obs) else self.PENALTY

    @staticmethod
    def create_maze():
        E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
        return [
            [B, B, B, B, B],
            [B, R, E, E, B],
            [B, B, B, E, B],
            [B, E, E, E, B],
            [B, B, B, E, B],
            [B, E, E, E, B],
            [B, B, B, B, B],
        ]


gym.envs.register(
    id="PointEMaze-v0",
    entry_point="mujoco_maze.maze_env:MazeEnv",
    kwargs=dict(
        model_cls=PointEnv,
        maze_task=GoalRewardEMaze,
        maze_size_scaling=GoalRewardEMaze.MAZE_SIZE_SCALING.point,
        inner_reward_scaling=GoalRewardEMaze.INNER_REWARD_SCALING,
    )
)

You can also customize models. See point.py or so.

Warning

Reacher enviroments are not tested.

[Experimental] Web-based visualizer

By passing a port like gym.make("PointEMaze-v0", websock_port=7777), one can use a web-based visualizer when calling env.render().

This feature is experimental and can produce some zombie proceses.

License

This project is licensed under Apache License, Version 2.0 (LICENSE or http://www.apache.org/licenses/LICENSE-2.0).