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Browse files- README.md +35 -0
- q-learning.pkl +3 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
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---
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tags:
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- FrozenLake-v1-4x4
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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-FrozenLake-v1-5x5-slippery
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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
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type: FrozenLake-v1-4x4
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metrics:
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- type: mean_reward
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value: 0.16 +/- 0.37
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name: mean_reward
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verified: false
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---
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# **Q-Learning** Agent playing1 **FrozenLake-v1**
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This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
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## Usage
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```python
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model = load_from_hub(repo_id="jbuch808/q-FrozenLake-v1-5x5-slippery", filename="q-learning.pkl")
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# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
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env = gym.make(model["env_id"])
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```
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q-learning.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:6f13e2ef4143bc7b8794781051bf5e6a48d1f254b8a0168ed7063afcd82b9127
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size 1190
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replay.mp4
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Binary file (69.5 kB). View file
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results.json
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{"env_id": "FrozenLake-v1", "mean_reward": 0.16, "n_eval_episodes": 100, "eval_datetime": "2023-10-13T01:14:59.627019"}
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