mazegym 1.0.2

A Gymnasium environment for maze-based reinforcement learning

MazeGym

A Gymnasium env for training reinforcement learning agents to navigate mazes.

Library used: .

9x9 maze

Random moves are used for this demo.

21x21 maze

Random moves are used for this demo.

35x15 maze

Random moves are used for this demo.

Usage

Initiating the env via gym

import gymnasium as gym

env_9x9_random = gym.make('Maze9x9Random-v0')
env_35x15_random = gym.make('Maze35x15Random-v0')

env_5x5_fixed = gym.make('Maze5x5Fixed-v0')
env_3x7_fixed = gym.make('Maze3x7Fixed-v0')

Initiating the env directly

from mazegym import MazeEnvironment

#Random env
env_random = MazeEnvironment(width=10, height=5)

# Fixed env
fixed_grid = np.ones((3, 7), dtype=np.int8)
fixed_grid[1, :] = 0
fixed_grid[1, 0] = 2
fixed_grid[1, 6] = 3
env_fixed = MazeEnvironment(grid=grid)

Making moves

import gymnasium as gym

env_35x15_random = gym.make('Maze35x15Random-v0')

# Reset the environment
observation, info = env_35x15_random.reset()

# Make a random valid move

valid_moves = info.get("valid_moves")
move = random.choice(valid_moves)
observation, reward, done, truncated, info = env_35x15_random.step(move)

# Render the environment. The only render mode is 'human' which renders visual output.
env_35x15_random.render()

# Close the environment
env_35x15_random.close()

Environment Details

• Action Space: Discrete(4) - Four possible actions: 0 (up), 1 (right), 2 (down), 3 (left). Invalid moves (moving into walls) results in an error.
• Observation Space: Box(0, 3, (height, width), int8). Contains values: 0 for empty paths, 1 for walls, 2 for the agent, 3 for the goal.
• Reward: 100 if the goal is reached, -1 for each step taken.
• Done: True if the agent reaches the goal, False otherwise.
• Truncated: True if maximum steps (3 × width × height) are exceeded, False otherwise.