Decentralized Policy Gradient

Decentralized policy gradient methods address the challenge of training multiple agents to cooperate in reinforcement learning environments without a central controller, aiming to optimize collective performance while respecting individual agent constraints and privacy. Current research focuses on developing efficient algorithms, such as momentum-based methods and those incorporating variance reduction techniques, often within actor-critic frameworks or employing softmax policies. These advancements are significant for applications requiring distributed control and privacy preservation, such as smart grids, autonomous vehicle coordination, and multi-robot systems.