shuojiang commited on
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Push Q-Learning agent to Hub

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Files changed (5) hide show
  1. .gitattributes +1 -0
  2. README.md +36 -0
  3. q-learning.pkl +3 -0
  4. replay.mp4 +3 -0
  5. results.json +1 -0
.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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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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+
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+ # **Q-Learning** Agent playing **FrozenLake-v1**
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+ This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
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+
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+ ## Usage
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+ ```python
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+ model = load_from_hub(repo_id="shuojiang/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
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+
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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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+ evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
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+
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+ ```
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+
q-learning.pkl ADDED
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+ size 914
replay.mp4 ADDED
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+ oid sha256:56ba66361f81e2d1dc4305e8bf921c20f263b0bc4d100906461f7c0c85364958
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+ size 31943
results.json ADDED
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+ {"env_id": "FrozenLake-v1", "mean_reward": 1.0, "n_eval_episodes": 100, "eval_datetime": "2022-10-12T14:09:18.021395"}