---
tags:
- FrozenLake-v1-4x4-no_slippery
- 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-no_slippery
      type: FrozenLake-v1-4x4-no_slippery
    metrics:
    - type: mean_reward
      value: 1.00 +/- 0.00
      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
```python
import gymnasium as gym
from huggingface_sb3 import load_from_hub
import numpy as np
import pickle

# Load the Q table
env_name = "FrozenLake-v1"
model_name = "q-FrozenLake-v1-4x4-noSlippery"
model_path = load_from_hub(repo_id="ch-bz/" + model_name, filename="q-learning.pkl")
Qtable = pickle.load(open(model_path, "rb"))["qtable"]

# Run the demonstration of the result
env = gym.make("FrozenLake-v1", map_name="4x4", is_slippery=False, render_mode="human")
state, info = env.reset()

while True:
    action = np.argmax(Qtable[state][:])
    state, reward, terminated, truncated, info = env.step(action)
    env.render()
    
    if terminated or truncated:
        state, info = env.reset()
```