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q-FrozenLake-v1-4x4-noSlippery
aspectcisco/q-FrozenLake-v1-4x4-noSlippery
q-FrozenLake-v1-4x4-noSlippery is a reinforcement learning model from aspectcisco. 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 .
Access
Public
Updated Nov 24, 2022
Repo size
32.9 KB
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At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Nov 24, 2022
- Updated
- Nov 24, 2022
- SHA
- 7b4fa377
Model card
From the Hugging Face model README
Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
Usage
model = load_from_hub(repo_id="aspectcisco/q-FrozenLake-v1-4x4-noSlippery", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
- Task
- Reinforcement Learning
- Created
- Nov 24, 2022
- Updated
- Nov 24, 2022