Flocking Multi-Agent is an open-source Python framework that implements Craig Reynolds’ flocking behaviors—alignment, cohesion, separation—and obstacle avoidance. It provides real-time visualization using Pygame, configurable agent parameters, and supports simulating large swarms. Developers and researchers can customize behaviors, integrate with robotics platforms, and analyze emergent group dynamics for simulation and educational purposes.
Flocking Multi-Agent is an open-source Python framework that implements Craig Reynolds’ flocking behaviors—alignment, cohesion, separation—and obstacle avoidance. It provides real-time visualization using Pygame, configurable agent parameters, and supports simulating large swarms. Developers and researchers can customize behaviors, integrate with robotics platforms, and analyze emergent group dynamics for simulation and educational purposes.
Flocking Multi-Agent offers a modular library for simulating autonomous agents exhibiting swarm intelligence. It encodes core steering behaviors—cohesion, separation and alignment—alongside obstacle avoidance and dynamic target pursuit. Using Python and Pygame for visualization, the framework allows adjustable parameters such as neighbor radius, maximum speed, and turning force. It supports extensibility through custom behavior functions and integration hooks for robotics or game engines. Ideal for experimentation in AI, robotics, game development, and academic research, it demonstrates how simple local rules lead to complex global formations.
Who will use Flocking Multi-Agent?
AI researchers studying swarm intelligence
Robotics engineers prototyping group behaviors
Game developers building NPC swarms
Students learning multi-agent systems
Educators demonstrating emergent behavior
How to use the Flocking Multi-Agent?
Step1: Clone the repository from GitHub
Step2: Install dependencies via pip (pygame, numpy)
Step3: Configure agent parameters in config.py
Step4: Run main.py to launch the simulation
Step5: Adjust behavior weights and visualize results
Platform
mac
windows
linux
Flocking Multi-Agent's Core Features & Benefits
The Core Features
Implementation of alignment, cohesion, and separation behaviors