---
tags:
- FrozenLake-v2-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v2-4x4-Slippery
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: FrozenLake-v2-4x4
      type: FrozenLake-v2-4x4
    metrics:
    - type: mean_reward
      value: 0.51 +/- 0.50
      name: mean_reward
      verified: false
---

  # **Q-Learning** Agent playing **FrozenLake-v2**
  - This is a trained model of a **Q-Learning** agent playing **FrozenLake-v2**. The **goal** of the agent is to navigate from starting state to the goal state by walking only on Frozen tiles and avoid holes.
  
  - The environment support two different sizes: `4x4` and `8x8`grids
  - The environment has two modes: 
    - `is_slippery=True` agent may not move in the desired direction due to the slippery nature of the tails.
    - `is_slippery=False` agent moves in the desired direction due to the non-slippery nature of the tails.
  
  - How to create the FrozenLake environment?
    - ```gym.make("FrozenLake-v1", desc=None, map_name="4x4", is_slippery=True, render_mode="rgb_array")```
    - `desc=None` specify non-predetermined maps, `map_name` will be used. You can also specify custom map like ```desc = ["SFFH", "HFHF", "FFHF", "HFFG"]```. If both are None, random `8x8` map with `80%` of locations frozen will be generated.

  ## Usage

  ```python

  model = load_from_hub(repo_id="HugeFighter/q-FrozenLake-v2-4x4-Slippery", 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"])
  ```