Maze Runner Game with Q-Learning

Table of Contents

1. Project Overview
2. Features
3. Prerequisites
4. Installation
5. Usage
6. Implementation Details
   • CustomMazeEnv Class
   • Environment Registration
7. Next Steps
8. Contributing
9. License
10. Contact
11. Acknowledgements

Project Overview

This project implements a basic version of a maze runner game trained using the Q-learning algorithm. The game is built using OpenAI Gym, providing a customized environment for reinforcement learning. The main components include a custom environment and a usage example.

Features

• Custom maze environment with configurable grid size
• Walls and goal position
• Four possible actions: up, right, down, left
• Reward system: positive reward for reaching the goal, small negative reward for each step
• Visualization of the maze and agent's position

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed Python 3.7 or later
• You have a Windows/Linux/Mac machine
• You have installed the required dependencies

Installation

To install the Maze Runner Game, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/maze-runner-qlearning.git
   

2. Navigate to the project directory:

   cd maze-runner-qlearning
   

3. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

4. Install the required dependencies:

   pip install -r requirements.txt
   

For more details on the project structure and implementation, see the Implementation Details section.

Usage

To use the Maze Runner Game:

1. Run the main script:

   python main.py
   

2. To use the environment in your own script:

import gym
env = gym.make('CustomMaze-v0')
env.reset()

done = False
while not done:
    action = env.action_space.sample()  # Take random action
    state, reward, done, _ = env.step(action)
    env.render()
    print(f"State: {state}, Reward: {reward}, Done: {done}")

This example demonstrates how to interact with the CustomMazeEnv using the Gym interface.

Implementation Details

CustomMazeEnv Class

The CustomMazeEnv class extends gym.Env and implements the following methods:

• __init__(): Initializes the environment with a 5x5 grid, sets the goal and wall positions.
• reset(): Resets the environment to the initial state.
• step(action): Executes the given action and returns the new state, reward, and done flag.
• render(): Visualizes the current state of the maze.

For usage examples, see the Usage section.

Environment Registration

The custom environment is registered with Gym using the ID 'CustomMaze-v0'. This allows you to create the environment using gym.make('CustomMaze-v0') as shown in the Usage section.

Next Steps

1. Implement the Q-learning algorithm
2. Train the agent to navigate the maze efficiently
3. Experiment with different maze configurations and learning parameters
4. Analyze and visualize the learning progress
5. Implement a more advanced reinforcement learning algorithm (e.g., Deep Q-Network)

For more information on how to contribute to these next steps, see the Contributing section.

Contributing

Contributions to this project are welcome. To contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b <branch_name>.
3. Make your changes and commit them: git commit -m '<commit_message>'
4. Push to the original branch: git push origin <project_name>/<location>
5. Create the pull request.

Alternatively, see the GitHub documentation on creating a pull request.

Before contributing, please review the project features and next steps to align your contributions with the project goals.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Contact

Author- Chirag Sindhwani

If you want to contact me, you can reach me at [email protected]. For bug reports or feature requests, please use the GitHub issue tracker.

Acknowledgements

• OpenAI Gym for the reinforcement learning framework
• NumPy for numerical computing
• Matplotlib for visualization

These libraries are essential for running the project. Make sure to install them as described in the Installation section.