Multi Agent Proximal Policy Optimization

Multi-agent proximal policy optimization (MAPPO) is a deep reinforcement learning approach designed to train multiple agents to collaborate effectively on complex tasks. Current research focuses on improving MAPPO's performance and scalability through techniques like attention mechanisms for better credit assignment, graph neural networks for representing agent interactions, and incorporating intent sharing or communication protocols to enhance coordination. These advancements are driving significant improvements in various applications, including traffic control, robotics, and resource management in wireless networks, by enabling more efficient and robust decentralized control systems.