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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-no_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-FrozenLake-v1-4x4-noSlippery
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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-no_slippery
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type: FrozenLake-v1-4x4-no_slippery
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metrics:
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- type: mean_reward
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value: 1.00 +/- 0.00
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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="AY00/q-FrozenLake-v1-4x4-noSlippery", 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 sha256:9e98a2445193951bbe55c4d271cd3af3deb7f204d9f8899f7872794548ec2640
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size 914
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replay.mp4
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Binary file (31.9 kB). View file
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results.json
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{"env_id": "FrozenLake-v1", "mean_reward": 1.0, "n_eval_episodes": 100, "eval_datetime": "2023-03-04T03:00:26.389930"}
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