MOBASwarm Agents

Welcome to MOBASwarm Agents, a framework designed to integrate SWARM algorithms with Large Language Models (LLMs) in a simulated MOBA (Multiplayer Online Battle Arena) environment using Unity. This framework aims to address complex task management and automation challenges by leveraging the strengths of decentralized SWARM algorithms and centralized LLMs for strategic oversight.

Table of Contents

- Introduction
- Features
- Getting Started
    Prerequisites
    Installation
- Usage
    Setting Up the Unity Environment
    Developing SWARM Algorithms
    LLM Integration
    Communication Protocols
- Contributing
- License

Introduction

MOBASwarm Agents is an innovative framework that combines the power of SWARM algorithms and LLMs within a MOBA-inspired simulation environment. The primary objective is to create a robust, scalable, and efficient system capable of managing and automating complex tasks.

Features

Simulated MOBA Environment: Utilize Unity's advanced graphical and simulation capabilities to create a dynamic task environment. SWARM Algorithms: Implement Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA) to manage tasks within designated lanes. LLM Integration: Use powerful language models for strategic decision-making and coordination. Efficient Communication: Implement ZeroMQ for robust inter-lane communication and shared memory for intra-lane communication. Visualization: Leverage Unity's visualization tools to monitor and analyze the interactions between SWARM algorithms and LLMs.

Getting Started

Prerequisites

Unity: Download and install Unity Hub and the latest LTS version of the Unity Editor from Unity. Python: Install Python 3.8 or later from the official Python website. Virtual Environment: Set up a Python virtual environment for managing dependencies.

bash

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

Installation

Clone the Repository:

bash

git clone https://github.com/yourusername/MOBASwarmAgents.git cd MOBASwarmAgents

Install Python Dependencies:

bash

pip install -r requirements.txt

Set Up Unity Project:
    Open Unity Hub and create a new project with the 3D template.
    Import necessary packages such as ML-Agents, TextMeshPro, etc.

Usage Setting Up the Unity Environment

Design the MOBA Map:
    Create lanes using 3D objects and Unity's NavMesh system for pathfinding.
    Place task nodes along the lanes and label them.

Implement Unity Scripts:
    Develop scripts to control SWARM algorithms and integrate them with the Unity environment.

Developing SWARM Algorithms

Ant Colony Optimization (ACO):
    Implement ACO using the DEAP library.
    Integrate ACO with Unity to manage tasks within designated lanes.

Particle Swarm Optimization (PSO):
    Implement PSO using the pyswarm library.
    Integrate PSO with Unity to optimize task parameters.

Genetic Algorithms (GA):
    Implement GA using the DEAP library.
    Integrate GA with Unity for evolutionary optimization tasks.

LLM Integration

Set Up LLM Framework:
    Install Hugging Face Transformers and other necessary libraries.
    Fine-tune an LLM for strategic decision-making.

Develop Integration Interface:
    Create interfaces for LLMs to interact with Unity and SWARM algorithms.

Communication Protocols

Install ZeroMQ:

bash

pip install pyzmq

Implement Communication:
    Set up message-passing with ZeroMQ for inter-lane communication.
    Use shared memory for intra-lane communication.

Contributing

We welcome contributions to MOBASwarm Agents! Please fork the repository and create a pull request with your changes. Ensure your code adheres to the project's coding standards and includes relevant tests. License

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