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
Evaluation results
- mean_reward on FrozenLake-v1-8x8-no_slipperyself-reported1.00 +/- 0.00