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---
tags:
- FrozenLake-v1-4x4-slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-table-frozen-lake
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-slippery
type: FrozenLake-v1-4x4-slippery
metrics:
- type: mean_reward
value: 0.75 +/- 0.43
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained **Q-Learning** agent playing **FrozenLake-v1**.
## Usage
```python
import gymnasium as gym
from huggingface_hub import snapshot_download
# https://github.com/libertininick/r2seedo
from r2seedo.io import load_n_verify_model
# Download model snapshot from Hugging Face Hub
repo_local_path = snapshot_download(
repo_id="libertininick/q-table-frozen-lake",
local_dir="path/to/download",
)
# Load the model from the snapshot
model = load_n_verify_model(repo_local_path)
# Create the environment
env = env_slippery = gym.make(
id='FrozenLake-v1',
map_name='4x4',
is_slippery=True,
)
```
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