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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: 295.51 +/- 20.94 |
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name: mean_reward |
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verified: false |
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--- |
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# **PPO** Agent playing **LunarLander-v2** |
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This is a trained model of a **PPO** agent playing **LunarLander-v2** |
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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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import gym |
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from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub |
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from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub. |
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from stable_baselines3 import PPO |
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from stable_baselines3.common.evaluation import evaluate_policy |
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from stable_baselines3.common.env_util import make_vec_env |
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# Create the environment |
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env = make_vec_env('LunarLander-v2', n_envs=16) |
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model = PPO( |
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policy = 'MlpPolicy', |
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env = env, |
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n_steps = 1024, |
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batch_size = 64, |
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n_epochs = 4, |
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gamma = 0.999, |
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gae_lambda = 0.98, |
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ent_coef = 0.01, |
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learning_rate=0.0004, |
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verbose=1) |
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# Train it for 3,000,000 timesteps |
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model.learn(total_timesteps=3000000) |
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# Save the model |
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model_name = "ppo-LunarLander-v2" |
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model.save(model_name) |
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... |
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``` |
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