Drone Swarm Reinforcement Learning with AirSim and StableBaselines3 🚀

Welcome to the Drone-Swarm-RL-airsim-sb3 repository! This project focuses on training drone swarms using cutting-edge technologies such as StableBaselines3, PettingZoo, AirSim, and Unreal Engine 4 (UE4). Our aim is to develop efficient algorithms for multi-agent reinforcement learning (MARL) that can control multiple drones in a simulated environment.

Table of Contents

• Introduction
• Technologies Used
• Installation
• Usage
• Training
• Results
• Contributing
• License
• Releases
• Contact

Introduction

Drone swarms represent a new frontier in robotics, allowing multiple drones to work together to accomplish tasks more efficiently than a single drone could. This project utilizes advanced reinforcement learning techniques to enable drones to learn optimal behaviors in a variety of scenarios.

The integration of AirSim provides a realistic simulation environment, while StableBaselines3 offers powerful algorithms for training our models. PettingZoo helps us manage multi-agent scenarios effectively.

Technologies Used

This project employs the following technologies:

• AirSim: A simulation platform for drones, enabling realistic physics and environments.
• Unreal Engine 4 (UE4): The game engine that powers AirSim, providing high-quality graphics and physics.
• StableBaselines3: A set of reliable implementations of reinforcement learning algorithms.
• PettingZoo: A library for multi-agent reinforcement learning environments.
• SuperSuit: A library that helps preprocess environments for reinforcement learning.

Key Topics

• AirSim
• Drone
• Droneswarm
• Multi-Agent Reinforcement Learning (MARL)
• Proximal Policy Optimization (PPO)
• Swarm Intelligence
• Swarm Robotics

Installation

To get started, clone this repository to your local machine:

git clone https://github.com/bensugursoy/Drone-Swarm-RL-airsim-sb3.git
cd Drone-Swarm-RL-airsim-sb3

Next, install the required dependencies. You can do this using pip:

pip install -r requirements.txt

Make sure you have AirSim and Unreal Engine 4 installed on your machine. Follow the official AirSim installation guide for detailed instructions.

Usage

After installing the necessary components, you can start the AirSim simulator. Open the Unreal Engine project and play the simulation. This will launch the AirSim environment where the drones will operate.

To run the training script, execute the following command:

python train.py

This will initiate the training process using the PPO algorithm from StableBaselines3. You can modify the training parameters in the config.py file to suit your needs.

Training

Training a drone swarm involves several steps:

1. Environment Setup: Configure the simulation environment in Unreal Engine.
2. Algorithm Selection: Choose an appropriate reinforcement learning algorithm (e.g., PPO).
3. Training Execution: Run the training script to begin the learning process.
4. Evaluation: Test the trained model in various scenarios to evaluate performance.

Training Parameters

You can adjust the following parameters in the config.py file:

• num_episodes: Total number of training episodes.
• max_steps: Maximum steps per episode.
• learning_rate: Learning rate for the optimizer.
• gamma: Discount factor for future rewards.

Results

After training, you can visualize the results using the provided scripts. The trained models will demonstrate how the drones collaborate to achieve their objectives. You can view performance metrics and graphs to analyze their behavior.

Example Results

Contributing

We welcome contributions to this project! If you have ideas for improvements or features, please fork the repository and submit a pull request. Make sure to follow the coding standards and include tests for new features.

Steps to Contribute

1. Fork the repository.
2. Create a new branch for your feature.
3. Make your changes.
4. Submit a pull request with a clear description of your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Releases

You can find the latest releases of this project here. Download the necessary files and execute them as needed.

For more detailed information on the releases, visit the Releases section on GitHub.

Contact

For questions or feedback, please reach out to the project maintainer. You can open an issue on GitHub or contact me directly.

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Thank you for exploring the Drone-Swarm-RL-airsim-sb3 repository! We hope you find it useful for your projects in drone swarm training and reinforcement learning.