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q-FrozenLake-v1-8x8-noSlippery
harshil128/q-FrozenLake-v1-8x8-noSlippery
q-FrozenLake-v1-8x8-noSlippery is a reinforcement learning model from harshil128. 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 . Compared to the 4x4 training model, I increased maxsteps to increase likelyhood of finding the goal during exploration. That still did not give a g…
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Updated Feb 3, 2023
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.mp4120 KB · 96%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Feb 3, 2023
- Updated
- Feb 3, 2023
- SHA
- dbed831e
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 . Compared to the 4x4 training model, I increased max_steps to increase likelyhood of finding the goal during exploration. That still did not give a good solution. Then I decreased decay rate to allow for more exploration and increased learning rate. But mostly it was the lower decay rate that improved the result.
Usage
model = load_from_hub(repo_id="harshil128/q-FrozenLake-v1-8x8-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
- Feb 3, 2023
- Updated
- Feb 3, 2023