Sample-Efficient Multi-Agent RL: An Optimization Perspective

Multi-agent reinforcement learning (MARL) has achieved re markable empirical successes in solving complicated games involving sequential and strategic d ecision-making across multiple agents ( Vinyals et al., 2019; Brown and Sandholm, 2018; Silver et al., 2016). These achievements have catalyzed many research efforts focusing on developing efficient MARL algorithms in a theoretically principled manner. Specifically, a multi-agent system is ty pically modeled as a general-sum Markov Game (MG) ( Littman, 1994), with the primary aim of efficiently discerning a certain equ ilibrium notion among multiple agents from data collected via online interactions. Some popular equilibrium notions include Nash equilibrium (NE), correlated equ ilibrium (CE), and coarse correlated equilibrium (CCE). However, multi-agent general-sum Markov Games (MGs) bring forth various challenges. In particular, empirical application suffers from the large st ate space. Such a challenge necessitates the use of the function approximation as an effective way to ex tract the essential features of RL problems and avoid dealing directly with the large state spa ce. Yet, adopting function approximation in a general-sum MG brings about additional complexities no t found in single-agent RL or a zero-sum MG.