readme: ---
  tags:
  - FrozenLake-v1-8x8-no_slippery
  - q-learning
  - reinforcement-learning
  - custom-implementation
  model-index:
  - name: q-FrozenLake-v1-8x8-non_slippery
    results:
    - metrics:
      - type: mean_reward
        value: 1.00 +/- 0.00
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: FrozenLake-v1-8x8-no_slippery
        type: FrozenLake-v1-8x8-no_slippery
  ---
  
    # **Q-Learning** Agent playing **FrozenLake-v1**
    This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
    
  
  n_training_episodes = 200000  # Total training episodes <br>
  learning_rate = 0.8          # Learning rate <br>
  
  # Evaluation parameters
  n_eval_episodes = 100        # Total number of test episodes <br>
  
  # Environment parameters <br>
  env_id = "FrozenLake-v1"     # Name of the environment <br>
  max_steps = 100               # Max steps per episode <br>
  gamma = 0.99                 # Discounting rate <br>
  eval_seed = []               # The evaluation seed of the environment <br>
  
  # Exploration parameters <br>
  epsilon = 1.0                 # Exploration rate <br>
  max_epsilon = 1.0             # Exploration probability at start <br>
  min_epsilon = 0.05            # Minimum exploration probability <br>
  decay_rate = 0.00005            # Exponential decay rate for exploration prob <br>
    
    ```
    