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README.md

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+---
+tags:
+- FrozenLake-v1-4x4-no_slippery
+- q-learning
+- reinforcement-learning
+- custom-implementation
+- SL-RL
+model-index:
+- name: q-FrozenLake-v1-4x4-no_slippery
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: FrozenLake-v1-4x4-no_slippery
+      type: FrozenLake-v1-4x4-no_slippery
+    metrics:
+    - type: mean_reward
+      value: 1.12 +/- 0.00
+      name: mean_perEpisode_SumStepReward
+      verified: false
+---
+
+            # sl_Sprout **Q-Learning** Agent playing **FrozenLake-v1**
+            This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
+
+            ## Usage
+
+            ```python
+
+            model = load_from_hub(repo_id="AllIllusion/q-FrozenLake-v1-4x4-no_slippery", filename="sl_TabularModel_FrozenLake-v1.pkl")
+
+            # Don't forget to check if you need to add additional attributes (is_slippery=False etc)
+            env = gym.make(model["FrozenLake-v1"])
+            ```
+        

results.json

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+{"env_id": "FrozenLake-v1", "numEpisode_Eval": 100, "mean_reward": 1.12, "std_reward": 0.0, "datetime_Eval": "2025-04-25T20:55:07.120654"}

sl_TabularModel_FrozenLake-v1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a3ff7ee3210604b83e7d4dbac64fb7448a6ea171d63c6ac8914696e91be84765
+size 808