Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances

Understand

Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control.

• With the recent development of (single-agent) deep RL, there is a resurgence of interests in developing new MARL algorithms, especially those that are backed by theoretical analysis.
• In this paper, we review some recent advances a sub-area of this topic: decentralized MARL with networked agents.
• Specifically, multiple agents perform sequential decision-making in a common environment, without the coordination of any central controller.