Maze Environment

Architecture

┌────────────────────────────────────┐
│ RL Training Code (Client)          │
│   MazeEnv.step(action)             │
└──────────────┬─────────────────────┘
               │ HTTP
┌──────────────▼─────────────────────┐
│ FastAPI Server (Docker)            │
│   MazeEnvironment                  │
│     ├─ Wraps Maze environment      │
│     └─ Agent controls player       │
└────────────────────────────────────┘

Installation & Usage

Option 1: Local Development (without Docker)

• Python 3.11+


• Numpy




from envs.maze_env import MazeEnv, MazeAction

Start local server manually

python -m envs.maze_env.server.app


Connect to local server

env = MazeEnv(base_url="http://localhost:8000")

Reset environment

result = env.reset()
print(f"Initial state: {result.observation.info_state}")
print(f"Legal actions: {result.observation.legal_actions}")

Take actions

for _ in range(10):
    action_id = result.observation.legal_actions[0]  # Choose first legal action
    result = env.step(MazeAction(action_id=action_id))
    print(f"Reward: {result.reward}, Done: {result.done}")
    if result.done:
        break

Cleanup

env.close()





Option 2: Docker (Recommended)



Build Docker image:



cd OpenEnv
docker build -f src/envs/maze_env/server/Dockerfile -t maze-env:latest .



Use with from_docker_image():



from envs.maze_env import MazeEnv, MazeAction

Automatically starts container

env = MazeEnv.from_docker_image("maze-env:latest")

result = env.reset()
result = env.step(MazeAction(action_id=0))

env.close()  # Stops container





Configuration





Variables




• maze : Maze as a numpy array saved in mazearray.py




Example





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





API Reference





MazeAction





@dataclass
class MazeAction(Action):
    action: int                        # Action to be taken





MazeObservation





@dataclass
class MazeObservation(Observation):
    position: List[int]  # [row, col]
    total_reward: float  # Total reward
    legal_actions: List[int] = field(default_factory=list)  # Legal action based on the current position





MazeState





@dataclass
class MazeState(State):
    episode_id: str     # Episode
    step_count: int     # Number of steps
    done: bool = False  # Solve status





References




• [Maze Environment](https://github.com/erikdelange/Reinforcement-Learning-Maze)