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casellimarco/
q-FrozenLake-v1-4x4-noSlippery-gamma_0

Name: q-FrozenLake-v1-4x4-noSlippery-gamma_0
Availability: InStock

FrozenLake-v1-4x4-no_slippery

q-learning

reinforcement-learning

custom-implementation

model-index

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Q-Learning Agent playing1 FrozenLake-v1

This is a trained model of a Q-Learning agent playing FrozenLake-v1 . With gamma=0 there is no information passed and so the only non zero value corresponds to the state just before the target.

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

Usage

python

model = load_from_hub(repo_id="casellimarco/q-FrozenLake-v1-4x4-noSlippery-gamma_0", 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"])