Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training
This is an incremental survey for researchers in multi-agent systems, summarizing existing methods without introducing new techniques.
The paper surveys recent multi-agent reinforcement learning algorithms that use centralized training with decentralized execution, exploring how different information sharing mechanisms affect group coordination in cooperative tasks.
Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.