具有势的非零和随机博弈的学习
Learning in Nonzero-Sum Stochastic Games with Potentials
摘要：
多智能体强化学习（MARL）在处理离散合作博弈场景中已经变得非常有效。然而，MARL还没有渗透到团队和零和游戏模型之外的环境中，将其限制在多智能体系统的一小部分。在本文中，我们介绍了新一代的MARL学习者，可以处理非零和支付结构和连续设置。特别是，我们研究的MARL问题在一类游戏称为随机潜在的游戏（SPGs）连续的状态-动作空间。与所有代理共享共同奖励的合作游戏不同，SPG能够对代理寻求实现其个人目标的真实世界场景进行建模。我们从理论上证明了我们的学习方法，SPot-AC，使独立的代理学习纳什均衡策略在多项式时间。我们证明了我们的框架解决了以前无法解决的任务，如协调导航和大型自私路由游戏，它优于最先进的MARL基线，如MADDPG和COMIX在这种情况下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
