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README.md

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+---
+tags:
+  - reinforcement-learning
+  - q-learning
+  - frozenlake
+license: mit
+library: gym
+---
+
+# Q-Learning Model for FrozenLake
+
+This model is a **Q-learning** agent trained to solve the **FrozenLake-v1** environment from OpenAI Gym.
+
+## Model Description
+
+The model uses Q-learning, a reinforcement learning algorithm, to navigate the FrozenLake environment. The agent learns by interacting with the environment, receiving rewards or penalties, and updating its Q-table accordingly.
+
+- **Environment**: FrozenLake-v1 (4x4 grid, no slippery surface)
+- **Algorithm**: Q-learning
+- **Action space**: 4 discrete actions (left, down, right, up)
+- **State space**: 16 discrete states (grid cells)
+- **Training duration**: Approximately [X hours] of training time.
+
+## Usage
+
+To use this model, you can load the trained Q-learning model from Hugging Face and run it in your environment.
+
+```python
+import gym
+from huggingface_hub import hf_hub_download
+import pickle
+
+# Load the model
+model_path = hf_hub_download(repo_id="willco-afk/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
+
+with open(model_path, 'rb') as f:
+    model = pickle.load(f)
+
+# Setup the environment
+env = gym.make("FrozenLake-v1", is_slippery=False)
+
+# Run your agent
+state = env.reset()
+done = False
+
+while not done:
+    action = model["qtable"].argmax(axis=1)[state]  # Choose the action with the highest Q-value
+    state, reward, done, info = env.step(action)
+
+    if done:
+        print(f"Episode finished with reward: {reward}")
+
 # Q-Learning Model for FrozenLake
 
 This model is a **Q-learning** agent trained to solve the **FrozenLake-v1** environment from OpenAI Gym.