Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees

Understand

Multi-agent reinforcement learning (MARL) has attracted much research attention recently.

• However, unlike its single-agent counterpart, many theoretical and algorithmic aspects of MARL have not been well-understood.
• In this paper, we study the emergence of coordinated behavior by autonomous agents using an actor-critic (AC) algorithm.
• Specifically, we propose and analyze a class of coordinated actor-critic algorithms (CAC) in which individually parametrized policies have a {\it shared} part (which is jointly optimized among all agents) and a {\it personalized} part (which is only locally optimized).