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q-FrozenLake-v1-4x4-noSlippery
InMDev/q-FrozenLake-v1-4x4-noSlippery
q-FrozenLake-v1-4x4-noSlippery is a reinforcement learning model from InMDev. 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 4, 2024
Repo size
914 B
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Repo makeup
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.mp431.1 KB · 90%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Nov 4, 2024
- Updated
- Nov 4, 2024
- SHA
- 6386be62
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 .
our Q-Learning agent is going to navigate from the starting state (S) to the goal state (G) by walking only on frozen tiles (F) and avoid holes (H).
Usage
model = load_from_hub(repo_id="InMDev/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"])
- Task
- Reinforcement Learning
- Created
- Nov 4, 2024
- Updated
- Nov 4, 2024