from huggingface_hub import load_from_hub
import gym

# Load the trained Q-learning model
model = load_from_hub(
    repo_id="KraTUZen/q-FrozenLake-v1-no-slippery",
    filename="q-learning.pkl"
)

# Initialize environment (ensure no_slippery flag is set)
env = gym.make(model["env_id"], is_slippery=False)

🧠 Notes

Mean Reward: 1.00 ± 0.00
Verification: Not yet independently verified

The agent is trained on the deterministic (no_slippery) version of FrozenLake.
Ensure you set is_slippery=False when creating the environment to reproduce results.
The model achieves optimal performance on the 4x4 FrozenLake grid.

📂 Repository Structure

q-learning.pkl → Serialized Q-table of the trained agent
README.md → Documentation and usage guide

✅ Results

The agent consistently reaches the goal state with perfect reward in evaluation runs, demonstrating stability and reproducibility in the deterministic environment.