In this paper, we consider the problem of large scale multi agent reinforcement learning. Firstly, we studied the representation problem of the pairwise value function to reduce the complexity of the interactions among agents. Secondly, we adopt a l2-norm trick to ensure the trivial term of the approximated value function is bounded. Thirdly, experimental results on battle game demonstrate the effectiveness of the proposed approach.

中文翻译：

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大规模多智能体强化学习的表示值函数方法

在本文中，我们考虑了大规模多智能体强化学习的问题。首先，我们研究了成对值函数的表示问题，以降低代理之间交互的复杂性。其次，我们采用 l2-norm 技巧来确保近似值函数的平凡项是有界的。第三，对战游戏的实验结果证明了所提出方法的有效性。