---
tags:
- CliffWalking-v0
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qlearning
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: CliffWalking-v0
      type: CliffWalking-v0
    metrics:
    - type: mean_reward
      value: -13.00 +/- 0.00
      name: mean_reward
      verified: false
---

# Q-Learning Agent playing CliffWalking-v0

This is a trained model of a Q-Learning agent playing **CliffWalking-v0**.
The agent was trained for 100000 episodes.

## Evaluation Results
- Mean Reward: -13.00 +/- 0.00

## Usage
```python
import gymnasium as gym
import pickle
from huggingface_hub import hf_hub_download

def load_from_hub(repo_id, filename):
    pickle_model = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(pickle_model, 'rb') as f:
        downloaded_model_file = pickle.load(f)
    return downloaded_model_file

model_data = load_from_hub(repo_id="dllmpg/qlearning", filename="q-learning.pkl")
q_table = model_data["qtable"]
env_id = model_data["env_id"]

# Example of running the loaded agent
env = gym.make(env_id)
raw_state, info = env.reset()
state_idx = raw_state  # CliffWalking uses direct state indexing
# ... run agent using greedy_policy(q_table, state_idx) ...
```