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# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .

## Usage
```python
model = load_from_hub(repo_id="cj-mills/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"])

evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])

```
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Evaluation results

  • mean_reward on FrozenLake-v1-4x4-no_slippery
    self-reported
    1.00 +/- 0.00