README.md

---
language: "en"
license: mit
tags:
  - reinforcement-learning
  - q-learning
  - gridworld
  - mountain-car
  - openai-gym
datasets:
  - gym
---

# Q-Learning in Reinforcement Learning Environments

## Project Overview
This project implements the Q-Learning algorithm in two different reinforcement learning environments: a custom-built Gridworld and the MountainCar-v0 environment from the OpenAI Gym library.

### Part 1: Custom Gridworld Environment
A simple 5x5 Gridworld where the goal is to navigate from the start state to the goal state, avoiding obstacles and minimizing the number of steps:
- **Grid Layout**:
  - Start at (0, 0)
  - Goal at (4, 4)
  - Obstacles at (2, 2) and (3, 3)
- **Rewards**:
  - Goal: +100
  - Obstacle: -10
  - Each step: -1

### Part 2: MountainCar-v0 Environment
Utilizing the standard MountainCar-v0 environment from the gym library, where the agent must drive a car up a steep hill:
- **Environment Features**:
  - The agent learns to balance momentum and gravity to reach the peak.
  - Visualize the environment using `env.render()`.

## Video Preview
Watch the demonstration video on [YouTube](https://www.youtube.com/watch?v=eLnngYsdluo).



## Implementation Details
- **Q-Learning**:
  - Initialize and update the Q-table using the Bellman equation.
  - An epsilon-greedy policy is used for action selection.
- **Training and Testing**:
  - Train the agent over multiple episodes.
  - Visualize and analyze the learned policy to evaluate performance.

## How to Use
1. **Requirements**: Ensure you have Python and the necessary libraries installed, including `gym` for the MountainCar environment.
2. **Setup**:
   - Clone the repository: `git clone https://huggingface.co/Karim2211/ReinforcementLearningModels/tree/main`
   - Install dependencies: `pip install -r requirements.txt`
3. **Running the Code**:
   - Navigate to the project directory.
   - For Gridworld: `python gridworld.py`
   - For MountainCar: `python mountain_car.py`
4. **Loading Models**:
   - Models are saved in the `models/` directory.
   - To load and test models:
     ```python
     import pickle
     # For Gridworld model
     with open('models/gridworld_model.pkl', 'rb') as f:
         gridworld_model = pickle.load(f)
     # For MountainCar model
     with open('models/mountain_car_model.pkl', 'rb') as f:
         mountain_car_model = pickle.load(f)
     ```
5. **Testing the Models**:
   - Use the loaded models to make predictions or to evaluate the policies.
   - Example testing code:
     ```python
     # Test Gridworld model
     gridworld.test_policy(gridworld_model)
     # Test MountainCar model
     mountain_car.test_policy(mountain_car_model)
     ```

## Dependencies
- Python 3.x
- gym
- numpy
- matplotlib (optional for visualization)


## Authors
- Karim Ashraf
- Shiref Elhalawany
- Mahitab Waleed

## License
This project is licensed under the MIT License.