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Download README.md from dopamineaddict/q-FrozenLake-v1-4x4-noSlippery: direct link, hf CLI and curl.
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- Download file 823 Bytes
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https://huggingface.co/dopamineaddict/q-FrozenLake-v1-4x4-noSlippery/resolve/main/README.md
- Command line
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hf download hf://dopamineaddict/q-FrozenLake-v1-4x4-noSlippery/README.md
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curl -L -o README.md https://huggingface.co/dopamineaddict/q-FrozenLake-v1-4x4-noSlippery/resolve/main/README.md
823 Bytes
metadata
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.28 +/- 0.45
name: mean_reward
verified: false
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="dopamineaddict/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"])