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--- |
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tags: |
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- FrozenLake-v1-4x4-slippery |
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- q-learning |
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- reinforcement-learning |
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- custom-implementation |
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model-index: |
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- name: q-table-frozen-lake |
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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: FrozenLake-v1-4x4-slippery |
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type: FrozenLake-v1-4x4-slippery |
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metrics: |
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- type: mean_reward |
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value: 0.75 +/- 0.43 |
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name: mean_reward |
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verified: false |
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--- |
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# **Q-Learning** Agent playing **FrozenLake-v1** |
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This is a trained **Q-Learning** agent playing **FrozenLake-v1**. |
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## Usage |
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```python |
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import gymnasium as gym |
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from huggingface_hub import snapshot_download |
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# https://github.com/libertininick/r2seedo |
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from r2seedo.io import load_n_verify_model |
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# Download model snapshot from Hugging Face Hub |
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repo_local_path = snapshot_download( |
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repo_id="libertininick/q-table-frozen-lake", |
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local_dir="path/to/download", |
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) |
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# Load the model from the snapshot |
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model = load_n_verify_model(repo_local_path) |
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# Create the environment |
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env = env_slippery = gym.make( |
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id='FrozenLake-v1', |
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map_name='4x4', |
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is_slippery=True, |
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) |
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``` |
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