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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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- metrics: |
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- type: mean_reward |
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value: 280.00 +/- 24.62 |
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name: mean_reward |
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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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--- |
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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** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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## Usage (with Stable-baselines3) |
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``` |
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model = PPO( |
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policy = 'MlpPolicy', |
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env = env, |
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n_steps = 2048, |
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batch_size = 512, |
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n_epochs = 4, |
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gamma = 0.099, |
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gae_lambda = 0.98, |
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ent_coef = 0.01, |
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learning_rate=0.00001, |
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verbose=1, |
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tensorboard_log="./ppo_tensorboard/") |
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model.learn(total_timesteps=int(10e6)) |
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``` |
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