README.md

metadata

library_name: stable-baselines3
tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: PPO
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: LunarLander-v2
          type: LunarLander-v2
        metrics:
          - type: mean_reward
            value: 244.16 +/- 18.09
            name: mean_reward
            verified: false

PPO Agent playing LunarLander-v2

This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.

Usage (with Stable-baselines3)

from stable_baselines3 import PPO
from stable_baselines3.common.monitor import Monitor
from huggingface_sb3 import load_from_hub

repo_id = "helamri/PPO-LunarLander-v2"
filename = "PPO-LunarLander-v2.zip"

checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, print_system_info=True)

eval_env = Monitor(gym.make("LunarLander-v2", render_mode="human"))

mean_rwd, std_rwd = evaluate_policy(model, eval_env, n_eval_episodes=10)
print(f"mean_reward: {mean_rwd}±{std_rwd}")