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Upload q-learning trained agent for FrozenLake-v1-4x4-slippery

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  1. .gitattributes +2 -0
  2. README.md +35 -0
  3. q-learning.pkl +3 -0
  4. replay.mp4 +3 -0
  5. results.json +1 -0
.gitattributes CHANGED
@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard 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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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ q-learning.pkl 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
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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-learning-FrozenLake-v1-4x4-slippery-params-v3
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+ results:
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+ - metrics:
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+ - type: mean_reward
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+ value: 0.90 +/- 0.30
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+ name: mean_reward
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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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+ ---
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+
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+ # **Q-Learning** Agent playing **FrozenLake-v1-4x4-slippery**
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+ This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1-4x4-slippery** .
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+
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+ ## Usage
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+ ```python
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+ model = load_from_hub(repo_id="linker81/q-learning-FrozenLake-v1-4x4-slippery-params-v3", 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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+ oid sha256:aa654981e7d10e2e7c147f078755bc39287b24acea3507c02eb3cdd06ca38e09
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+ size 914
replay.mp4 ADDED
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+ size 32779
results.json ADDED
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+ {"env_id": "FrozenLake-v1", "mean_reward": 0.9036144578313253, "n_eval_episodes": 100, "eval_datetime": "2022-05-22T08:03:12.226059"}