在本文中，我們研究了具有兩個團隊的「團隊對稱賽局」。同一團隊內的玩家擁有共同的收益函數。我們證明了團隊對稱賽局總是存在一個團隊對稱的納許均衡。我們針對團隊對稱納許均衡，提出並解決了一個線性互補問題。我們還為團隊對稱賽局提出了一種基於行為評論(Actor-Critic)的多智能體增強式學習演算法。透過實驗模擬，我們證明了這種演算法的性能優於許多現有的演算法。程式碼已公布在https://github.com/Forcer0625/DelAC

In this paper we study team-symmetric games with two teams. Players within the same team have common payoff functions. We show that team symmetric games always have a team-symmetric Nash equilibrium. We de velop 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. The code is available at https://github.com/Forcer0625/DelAC