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q-FrozenLake-v1-4x4-noSlippery-gamma_0
casellimarco/q-FrozenLake-v1-4x4-noSlippery-gamma_0
q-FrozenLake-v1-4x4-noSlippery-gamma_0 is a reinforcement learning model from casellimarco. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
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.
Access
Public
Updated May 12, 2023
Repo size
907 B
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.mp439.1 KB · 91%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- May 12, 2023
- Updated
- May 12, 2023
- SHA
- 0c24d327
Model card
From the Hugging Face model README
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
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"])
- Task
- Reinforcement Learning
- Created
- May 12, 2023
- Updated
- May 12, 2023