论文标题
代表大规模多代理增强学习的价值功能方法
Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning
论文作者
论文摘要
在本文中，我们考虑了大型多代理增强学习的问题。首先，我们研究了成对值函数的表示问题，以降低代理之间相互作用的复杂性。其次，我们采用L2-norm技巧来确保近似值函数的微不足道项的界限。第三，战斗游戏的实验结果证明了拟议方法的有效性。
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.
