2021/03/16 by David Mguni, Mguni, David, Yutong Wu +15 · 3 citations
Computer Science · Decision Sciences · #Auction Theory and Applications #Game Theory and Applications #Reinforcement Learning in Robotics #cs.MA
paper · pdf · doi:10.48550/arxiv.2103.09284
ICML 2021
arxiv created 2021/06/15 · arxiv updated 2021/06/16
Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by team and zero-sum games, confining it to a small subset of multi-agent systems. In this paper, we introduce a new generation of MARL learners that can handle nonzero-sum payoff structures and continuous settings. In particular, we study the MARL problem in a class of games known as stochastic potential games (SPGs) with continuous state-action spaces. Unlike cooperative games, in which all agents share a common reward, SPGs are capable of modelling real-world scenarios where agents seek to fulfil their individual goals. We prove theoretically our learning method, SPot-AC, enables independent agents to learn Nash equilibrium strategies in polynomial time. We demonstrate our framework tackles previously unsolvable tasks such as Coordination Navigation and large selfish routing games and that it outperforms the state of the art MARL baselines such as MADDPG and COMIX in such scenarios.