FrozenLake-v1-4x4-no_slippery q-learning reinforcement-learning custom-implementation

Q-Learning Agent playing1 FrozenLake-v1

This is a trained model of a Q-Learning agent playing FrozenLake-v1 . With gamma=1 the Q table is

array([[1., 1., 1., 1.],
     [1., 0., 1., 1.],
     [1., 1., 1., 1.],
     [1., 0., 1., 1.],
     [1., 1., 0., 1.],
     [0., 0., 0., 0.],
     [0., 1., 0., 1.],
     [0., 0., 0., 0.],
     [1., 0., 1., 1.],
     [1., 1., 1., 0.],
     [1., 1., 0., 1.],
     [0., 0., 0., 0.],
     [0., 0., 0., 0.],
     [0., 1., 1., 1.],
     [1., 1., 1., 1.],
     [0., 0., 0., 0.]])

Usage


model = load_from_hub(repo_id="casellimarco/q-FrozenLake-v1-4x4-noSlippery-gamma_1", filename="q-learning.pkl")

# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])