--- 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: 289.96 +/- 22.59 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](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) ```python import gym from stable_baselines3 import PPO from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="dmenini/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip" ) model = PPO.load(checkpoint) env = gym.make("LunarLander-v2") # Evaluate the agent and watch it eval_env = gym.make("LunarLander-v2") mean_reward, std_reward = evaluate_policy( model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False ) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") ```