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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: 285.14 +/- 21.10 |
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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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Made as part of the Deep RL course: https://huggingface.co/learn/deep-rl-course. Tuned with Optuna, as introduced in the course. This is my first successful attempt of using Optuna, so do not expect the code or parameters to be ideal! |
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I was able to improve upon my result from Unit1, https://huggingface.co/humnrdble/DeepRL-unit1. Both models were trained for 1500000 steps. The video of my first attempt certainly looks smoother, but scores worse. |
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The code is available in unit1-notebook-tuned.ipynb, but no attempt was made to make it particularly legible. |
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Hyperparameters deviating from the Stable-baselines3 baseline: |
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- gamma: 1-0.006075594024321983 |
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- max_grad_norm: 1.8559426752164974 |
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- exponent_n_steps: 9 (i.e. 2**9 steps) |
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- learning_rate: 0.0011176199638550707 |
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## Usage (with Stable-baselines3) |
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TODO: Add your code |
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```python |
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from stable_baselines3 import ... |
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from huggingface_sb3 import load_from_hub |
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... |
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``` |
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