Battlefield Agent Alliance Decision-Making Two Layer Reinforcement Learning Algorithm

摘要：

In the background of Agent Alliance combat deduction, here we present a Two Layer Reinforcement learning algorithm, referred to a TLRL algorithm, for the special requirements of battlefield simulation environment Agents offensive and defensive decision-making study. The algorithm model is classified into two layers: one is the global decision-making Agent, called Commandant Agent, learning from the environment as well as both enemies and friends actions, the other is the Servant Agents optimizing the action by receiving local environment feedback. Finally the war situation deduction which is carried out on the simulation platform TBS we set up, has showed the fast convergence and effectiveness of this algorithm.

关键词： battlefield agent alliance decision-making reinforcement learning

作者: Xie Zhi-jun Dong Chao-yang Yang Fei Chen Wei

作者单位: School of Automation Science and Electrical Engineering Beihang University Beijing, China School of Aerospace Science and Engineering Beihang University Beijing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 174-178

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）