src/envs/maze_env/README.md

Maze Environment

Integration of Maze game with the OpenEnv framework.

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)

Requirements:

• 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