--- 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
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 = 100 # Max steps per episode
gamma = 0.99 # Discounting rate
eval_seed = [] # The evaluation seed of the environment
# Exploration parameters
epsilon = 1.0 # Exploration rate
max_epsilon = 1.0 # Exploration probability at start
min_epsilon = 0.05 # Minimum exploration probability
decay_rate = 0.00005 # Exponential decay rate for exploration prob
```