Maze Environment

A gridworld maze where the agent must navigate from a start cell to an exit while avoiding walls.

Maze Layout

The default environment ships with a single 8x8 maze:

0 0 0 0 0 1 0 0
1 1 0 1 0 1 0 1
0 0 0 1 0 0 0 1
0 1 1 1 1 1 0 0
0 0 0 0 0 0 1 0
1 0 1 1 1 0 0 0
0 0 0 0 1 0 1 0
0 1 1 0 0 0 0 0

0 is an empty cell and 1 is a wall. Coordinates are (col, row) with (0, 0) at the upper-left.

Quick Start

The simplest way to use the Maze environment is through the MazeEnv client:

from maze_env import MazeAction, MazeEnv

try:
    # Create environment from Docker image
    env = MazeEnv.from_docker_image("maze_env-env:latest").sync()

    # Reset to start a new episode
    result = env.reset()
    print(f"Start position: {result.observation.current_position}")
    print(f"Legal actions: {result.observation.legal_actions}")

    # Play until done
    while not result.done:
        action_id = result.observation.legal_actions[0]
        result = env.step(MazeAction(action=action_id))
        print(f"Position: {result.observation.current_position}")
        print(f"Reward: {result.reward}, Done: {result.done}")

finally:
    # Always clean up
    env.close()

That’s it! The MazeEnv.from_docker_image() method handles:

• Starting the Docker container
• Waiting for the server to be ready
• Connecting to the environment
• Container cleanup when you call close()

Building the Docker Image

From the environment directory (envs/maze_env/):

docker build -t maze_env-env:latest -f server/Dockerfile .

Deploying to Hugging Face Spaces

From envs/maze_env/, run openenv push (or openenv push --repo-id my-org/maze-env). See the openenv push reference for all options.

Running the Default Maze

docker run -p 8000:8000 maze_env-env:latest

Environment Details

Action

MazeAction: Contains a single field

• action (int) - Movement action
  • 0 = move up
  • 1 = move down
  • 2 = move left
  • 3 = move right

Observation

MazeObservation: Per-step observation payload

• current_position (List[int]) - Agent position as [col, row]
• legal_actions (List[int]) - Valid actions from the current position
• metadata (dict) - Extra info:
  • maze: 2D grid (0 = empty, 1 = wall)
  • status: “playing”, “win”, or “lose”
  • exit_cell: Exit position as [col, row]
  • step: Current step count

State

MazeState: Server-side state snapshot

• episode_id (str) - Unique identifier for the current episode
• step_count (int) - Number of steps taken
• done (bool) - Whether the episode has ended
• current_position (List[int]) - Current agent position
• exit_cell (List[int]) - Exit position
• status (str) - “playing”, “win”, or “lose”

Reward

The reward follows the underlying maze rules:

• Small penalty for a move (-0.05)
• Penalty for revisiting a cell (-0.25)
• Penalty for invalid move (-0.75)
• Reward for reaching the exit (+10.0)

The episode ends with lose when the accumulated reward drops below -0.5 * maze.size (-32 for the default maze).

Configuration

The server takes no environment variables. To use another layout, construct MazeEnvironment(maze_array=..., start_cell=(col, row), exit_cell=(col, row)) in server/app.py. The exit defaults to the bottom-right cell.

Advanced Usage

Connecting to an Existing Server

If you already have a Maze environment server running:

from maze_env import MazeAction, MazeEnv

# Connect to existing server
env = MazeEnv(base_url="http://localhost:8000")

# Use as normal
result = env.reset()
result = env.step(MazeAction(action=result.observation.legal_actions[0]))

# Close connection (does NOT stop the server)
env.close()

Connecting to HuggingFace Space

from maze_env import MazeAction, MazeEnv

# Connect to remote Space
env = MazeEnv(base_url="https://your-username-maze-env.hf.space")

result = env.reset()
print(f"Position: {result.observation.current_position}")
print(f"Legal actions: {result.observation.legal_actions}")

result = env.step(MazeAction(action=result.observation.legal_actions[0]))
env.close()

References

• Reinforcement-Learning-Maze (original implementation)