---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q_FrozenLake_v1_4x4_noSlippery
  results:
  - metrics:
    - type: mean_reward
      value: 1.00 +/- 0.00
      name: mean_reward
    task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: FrozenLake-v1-4x4-no_slippery
      type: FrozenLake-v1-4x4-no_slippery
---


# 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="jianzhnie/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"])

```