Q-Learning with Optuna Bayesian Optimization on FrozenLake-v1
This model features a tuned Q-Learning Table with hyperparameters optimized via Optuna (Bayesian Search) on the deterministic FrozenLake-v1 (4x4) environment, demonstrating automated hyperparameter tuning for the Hugging Face Deep RL Course (Unit 2).
🚀 Model Details
- Environment: Gymnasium
FrozenLake-v1(is_slippery=False) - Optimization: Optuna Bayesian Hyperband Search (100 trials)
- Success Rate: 100% (1.00 / 1.00)
Evaluation results
- mean_reward on FrozenLake-v1self-reported1.00 +/- 0.00