---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: FrozenLake-v1-8x8
      type: FrozenLake-v1-8x8
    metrics:
    - type: mean_reward
      value: 0.09 +/- 0.29
      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

  model = load_from_hub(repo_id="MattStammers/q-FrozenLake-v1-8x8-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"])
  ```

  This one is not easy to build with just a Q-table. It has taken a lot of training even to get him to occasionally slip into the prize. 

  To optimise him even further is probably going to take a different approach. To get this result I trained using the following parameters:

  ```python
{'env_id': 'FrozenLake-v1',
 'max_steps': 200,
 'n_training_episodes': 1000000,
 'n_eval_episodes': 100,
 'eval_seed': [],
 'learning_rate': 0.9,
 'gamma': 0.99,
 'max_epsilon': 1,
 'min_epsilon': 0.05,
 'decay_rate': 0.0005,
```