---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery-weak
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: FrozenLake-v1-8x8-no_slippery
      type: FrozenLake-v1-8x8-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

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

  To make this Q-learning agent work requires more extended training; otherwise the agent never successfully reaches the end goal and convergence does not take place.

  In my case I found 50 million training steps sufficient with the following hyperparameters:

```python
# Training parameters
n_training_episodes = 50000000  # Total training episodes
learning_rate = 0.99           # Learning rate

# Evaluation parameters
n_eval_episodes = 100        # Total number of test episodes

# Environment parameters
env_id = "FrozenLake-v1"     # Name of the environment
max_steps = 200             # Max steps per episode
gamma = 0.99                 # Discounting rate
epsilon = 0.1                # Ideal Episolon
eval_seed = []               # The evaluation seed of the environment

# Exploration parameters
max_epsilon = 1             # Exploration probability at start
min_epsilon = 0.05            # Minimum exploration probability
decay_rate = 0.0005            # Exponential decay rate for exploration prob

```