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README.md

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+---
+tags:
+- FrozenLake-v1-4x4
+- q-learning
+- reinforcement-learning
+- custom-implementation
+model-index:
+- name: q-learning_FrozenLake-v1_slippery4x4_v0
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: FrozenLake-v1-4x4
+      type: FrozenLake-v1-4x4
+    metrics:
+    - type: mean_reward
+      value: 0.64 +/- 0.48
+      name: mean_reward
+      verified: false
+---
+
+      # **Q-Learning** Agent playing1 **FrozenLake-v1**
+      This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
+
+      ## Usage
+
+      ```python
+
+      model = load_from_hub(repo_id="achgls/q-learning_FrozenLake-v1_slippery4x4_v0", filename="q-learning.pkl")
+
+      # Don't forget to check if you need to add additional attributes (is_slippery=False etc)
+      env = gym.make(model["env_id"])
+      ```
+      

q-learning.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33556f93256dec16eaed919349d20aae757242b8e397e3db38e88062eaff12c3
+size 702

results.json

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+{"env_id": "FrozenLake-v1", "mean_reward": 0.64, "n_eval_episodes": 100, "eval_seed": [16, 54, 165, 177, 191, 191, 120, 80, 149, 178, 48, 38, 6, 125, 174, 73, 50, 172, 100, 148, 146, 6, 25, 40, 68, 148, 49, 167, 9, 97, 164, 176, 61, 7, 54, 55, 161, 131, 184, 51, 170, 12, 120, 113, 95, 126, 51, 98, 36, 135, 54, 82, 45, 95, 89, 59, 95, 124, 9, 113, 58, 85, 51, 134, 121, 169, 105, 21, 30, 11, 50, 65, 12, 43, 82, 145, 152, 97, 106, 55, 31, 85, 38, 112, 102, 168, 123, 97, 21, 83, 158, 26, 80, 63, 5, 81, 32, 11, 28, 148], "eval_datetime": "2023-05-01T16:35:49.058055"}