---
tags:
- reinforcement-learning
- q-learning
- gymnasium
- cliffwalking
library_name: gymnasium
license: apache-2.0
---

# CliffWalking Q-Learning Agent

This repository contains a Q-learning agent trained on the **CliffWalking-v0** environment from **Gymnasium**. The agent learns to navigate the cliff, avoiding falling into the cliff zone while reaching the goal with minimal penalties. The Q-learning algorithm is implemented with epsilon-greedy exploration and updates the Q-table based on state-action-reward transitions.

## Files:
- `train.py`: The main script that trains the Q-learning agent.
- `cliffWalking_qtable.npy`: The saved Q-table after training.
- `replay.mp4`: A video of the agent's performance after training.
  
## Training Details:
- **Environment**: `CliffWalking-v0` (Gymnasium)
- **Episodes**: 30,000
- **Learning Rate (α)**: 0.2
- **Discount Factor (γ)**: 0.97
- **Epsilon (ε)**: 0.2 (exploration vs exploitation trade-off)

The agent starts by exploring the environment randomly and gradually learns the optimal path to avoid falling off the cliff while reaching the goal.

## How to Run:

### 1. Install Dependencies:

Make sure you have the required packages installed:
```bash
pip install gymnasium numpy imageio[ffmpeg]
```

### 2. Training the Agent:
To train the agent, run the script train.py:

```bash
python train.py
```