Push Q-Learning agent for CliffWalking-v0

README.md

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+---
+tags:
+- CliffWalking-v0
+- q-learning
+- reinforcement-learning
+- custom-implementation
+model-index:
+- name: qlearning
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CliffWalking-v0
+      type: CliffWalking-v0
+    metrics:
+    - type: mean_reward
+      value: -13.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# Q-Learning Agent playing CliffWalking-v0
+
+This is a trained model of a Q-Learning agent playing **CliffWalking-v0**.
+The agent was trained for 100000 episodes.
+
+## Evaluation Results
+- Mean Reward: -13.00 +/- 0.00
+
+## Usage
+```python
+import gymnasium as gym
+import pickle
+from huggingface_hub import hf_hub_download
+
+def load_from_hub(repo_id, filename):
+    pickle_model = hf_hub_download(repo_id=repo_id, filename=filename)
+    with open(pickle_model, 'rb') as f:
+        downloaded_model_file = pickle.load(f)
+    return downloaded_model_file
+
+model_data = load_from_hub(repo_id="dllmpg/qlearning", filename="q-learning.pkl")
+q_table = model_data["qtable"]
+env_id = model_data["env_id"]
+
+# Example of running the loaded agent
+env = gym.make(env_id)
+raw_state, info = env.reset()
+state_idx = raw_state  # CliffWalking uses direct state indexing
+# ... run agent using greedy_policy(q_table, state_idx) ...
+```

q-learning.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec4e75d164a79a5f852ff518b01223b5b959cbdb6e6a1cfd07d139763182db64
+size 2399

results.json

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+{"env_id": "CliffWalking-v0", "mean_reward": -13.0, "std_reward": 0.0, "n_eval_episodes": 100, "eval_datetime": "2025-06-15T22:29:06.965639"}