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q-FrozenLake-v1-8x8-Slippery
MattStammers/q-FrozenLake-v1-8x8-Slippery
q-FrozenLake-v1-8x8-Slippery 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
2.4 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
- d2659cf4
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", 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"])
This one is not easy to build with just a Q-table. It has taken a lot of training even to get him to occasionally slip into the prize.
To optimise him even further is probably going to take a different approach. To get this result I trained using the following parameters:
{'env_id': 'FrozenLake-v1',
'max_steps': 200,
'n_training_episodes': 1000000,
'n_eval_episodes': 100,
'eval_seed': [],
'learning_rate': 0.9,
'gamma': 0.99,
'max_epsilon': 1,
'min_epsilon': 0.05,
'decay_rate': 0.0005,
- Task
- Reinforcement Learning
- Created
- Aug 15, 2023
- Updated
- Aug 15, 2023