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| Rank | Model | Mean Reward | Std Dev | Success Rate | Notes |
|---|---|---|---|---|---|
| 🥇 1 | MCTS | 27.4 | 3.64 | 1.00 | 50 simulations, random rollout |
| 2 | PPO | 4.0 | 5.83 | ~0.40 | Trained with Stable-Baselines3 |
| 3 | DQN | –30.0 | 23.9 | ❌ | Failed task consistently |
pip install -r requirements.txt1git clone https://huggingface.co/spaces/YOUR_USERNAME/firefighter-gridworld-leaderboard
2cd firefighter-gridworld-leaderboardpython evaluation/evaluate_custom_agent.py --path ./my_agent.zip --algo PPOeval_results.json via Pull Request.1import gymnasium as gym
2from env.firefighter_env import FireFighterEnv
3
4env = FireFighterEnv()
5obs, info = env.reset()
6for _ in range(60):
7 action = env.action_space.sample()
8 obs, reward, terminated, truncated, info = env.step(action)
9 if terminated or truncated:
10 breakeval_results.jsonenv/ – environment codeagents/ – training scripts (PPO, DQN, MCTS)evaluation/ – evaluation and renderingmodels/ – saved agentsassets/ – sprites and animation