readme: ---
  tags:
  - FrozenLake-v1-4x4
  - q-learning
  - reinforcement-learning
  - custom-implementation
  model-index:
  - name: q-FrozenLake-v1-custom-map-Slippery-edition
    results:
    - task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: FrozenLake-v1-4x4
        type: FrozenLake-v1-4x4
      metrics:
      - type: mean_reward
        value: 0.89 +/- 0.31
        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="kinkpunk/q-FrozenLake-v1-custom-map-Slippery-edition",
                          filename="q-learning.pkl")
  
    # Don't forget to change additional attributes
    # when you create environment using 4x4 map
    env = gym.make('FrozenLake-v1',
                    desc=["SFFF", "FHHF", "FFHF", "HFFG"],
                    is_slippery=True)
    ```
    
    ## Training parameters
  
    ```python
    # Training parameters
    n_training_episodes = 105000  # Total training episodes
    learning_rate = 0.8          # Learning rate
  
    # Evaluation parameters
    n_eval_episodes = 100        # Total number of test episodes
  
    # Environment parameters
    env_id = "FrozenLake-v1"     # Name of the environment
    max_steps = 99               # Max steps per episode
    gamma = 0.98                 # Discounting rate
    eval_seed = []               # The evaluation seed of the environment
  
    # Exploration parameters
    max_epsilon = 0.99            # Exploration probability at start
    min_epsilon = 0.02            # Minimum exploration probability 
    decay_rate = 0.009            # Exponential decay rate for exploration prob
    
    ```