README.md

metadata

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