Collaborative Strategy of Multi-Agent Deep Reinforcement Learning for Complex Tasks

The rapid development of intelligent systems has made the research on collaborative optimization among multiple agents particularly important in multiple application fields such as multi-robot collaboration, intelligent transportation, and resource allocation. Traditionally, rule-based or model-based approaches have been difficult to achieve good scalability and adaptability in a constantly changing environment. In such situations, multi-agent reinforcement learning (MARL) can learn how to behave in collaboration by constantly interacting with their surroundings. This paper reviews basic frameworks for MARL like centralized learning, decentralized learning and centralized training with decentralized execution. It also looks into difficulties like environmental changes, working together well, and making good training. In summary, this paper gives directions for future work on coordination mechanism and communication strategy, and also makes MARL architecture can be used more widely in future application.