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q-FrozenLake-v1-8x8-Slippery-take3
MattStammers/q-FrozenLake-v1-8x8-Slippery-take3
q-FrozenLake-v1-8x8-Slippery-take3 is a reinforcement learning model from MattStammers. 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 Aug 15, 2023
Repo size
4.9 KB
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.mp4159 KB · 97%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Aug 15, 2023
- Updated
- Aug 15, 2023
- SHA
- 3a6e2298
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 .
Usage
model = load_from_hub(repo_id="MattStammers/q-FrozenLake-v1-8x8-Slippery-take3", 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"])
{'env_id': 'FrozenLake-v1', 'max_steps': 200, 'n_training_episodes': 1000000, 'n_eval_episodes': 100, 'eval_seed': [], 'learning_rate': 0.2, 'gamma': 0.99, 'max_epsilon': 1, 'min_epsilon': 0.05, 'decay_rate': 0.0005}
Reduction in the learning rate and extension of training episodes seems to be producing better results. If one further attempt doesn't best this I will leave it there
- Task
- Reinforcement Learning
- Created
- Aug 15, 2023
- Updated
- Aug 15, 2023