In this paper we study team-symmetric games with $m\ge 2$ teams. Players within a team have symmetric identity and have a common payoff function. We show that team-symmetric games always have a team-symmetric Nash equilibrium. We develop and solve a linear complementarity problem of team-symmetric Nash equilibria. We propose an actor-critic based multi-agent reinforcement learning algorithm for team-symmetric games. Through simulations, we show that this multi-agent reinforcement learning algorithm performs much better than many existing algorithms.

翻译：本文研究具有$m\ge 2$个团队的团队对称博弈。团队内的智能体具有对称身份且共享相同的收益函数。我们证明了团队对称博弈必然存在团队对称纳什均衡，并构建并求解了团队对称纳什均衡的线性互补问题。我们提出了一种基于演员-评论家框架的团队对称博弈多智能体强化学习算法。仿真实验表明，该多智能体强化学习算法的性能显著优于许多现有算法。