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--- |
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library_name: stable-baselines3 |
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tags: |
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- LunarLander-v2 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- stable-baselines3 |
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model-index: |
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- name: PPO |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: LunarLander-v2 |
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type: LunarLander-v2 |
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metrics: |
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- type: mean_reward |
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value: 330.99 +/- 20.23 |
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name: mean_reward |
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verified: true |
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--- |
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# **PPO** Agent playing **LunarLander-v2** |
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A trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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## Usage (with Stable-baselines3) |
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```python |
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from stable_baselines3 import PPO |
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from stable_baselines3.common.env_util import make_vec_env |
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from stable_baselines3.common.evaluation import evaluate_policy |
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from huggingface_sb3 import load_from_hub |
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# Download the model checkpoint |
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model_checkpoint = load_from_hub("DeathReaper0965/ppo-mlp-LunarLander-v2", "ppo-mlp-LunarLander-v2.zip") |
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# Create a vectorized environment |
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env = make_vec_env("LunarLander-v2", n_envs=1) |
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# Load the model |
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model = PPO.load(model_checkpoint, env=env) |
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# Evaluate |
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print("Evaluating model") |
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mean_reward, std_reward = evaluate_policy( |
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model, |
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env, |
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n_eval_episodes=30, |
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deterministic=True, |
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) |
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print(f"Mean reward = {mean_reward:.2f} +/- {std_reward}") |
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# Start a new episode |
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obs = env.reset() |
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try: |
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while True: |
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action, state = model.predict(obs, deterministic=True) |
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obs, reward, done, info = env.step(action) |
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env.render() |
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except KeyboardInterrupt: |
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pass |
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``` |
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## Conclusion |
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The above steps ensure that the Agent Model gets downloaded successfully. <br> |
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You may need to download the required libraries and packages specific to your operating system to resume training from the supplied checkpoint and run it for more steps. |
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