README.md

---
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** .

## Codes

Github repos(Give a star if found useful):
  * https://github.com/hishamcse/DRL-Renegades-Game-Bots
  * https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
  * https://github.com/hishamcse/Robo-Chess

Kaggle Notebook:
  * https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-2-frozenlake-v1-taxi-v3

## Usage

```python

model = load_from_hub(repo_id="hishamcse/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"])
  ```