Multi-Agent Reinforcement Learning Algorithm Design Toolkit

The Multi-Agent Toolkit is a comprehensive framework that enables seamless design and simulation of multi-agent reinforcement learning algorithms. It provides a suite of functions and interfaces—including environment wrappers, agent controllers, and communication protocols—that simplify the development and testing of multi-agent systems. Through customizable agent classes and environment templates, users can rapidly construct multi-agent models with configurable observation/action spaces. The toolkit supports various reinforcement learning algorithms (e.g., MADDPG, QMIX, COMA) with built-in training loops and evaluation metrics. Extensive documentation and example codes demonstrate practical implementations, such as centralized training with decentralized execution paradigms and reward shaping techniques. This toolkit serves as an essential resource for advancing research and development in multi-agent reinforcement learning, offering both scalability for complex scenarios and debugging tools for performance optimization.