国防科技大学学报2011，Vol.33Issue(1)：53-58,6.
一种基于多Agent强化学习的多星协同任务规划算法
An Algorithm of Cooperative Multiple Satellites Mission Planning Based on Multi-agent Reinforcement Learning
摘要
Abstract
A multi-satellite cooperative planning problem model was given considering the characteristics of the task requests and satellite constraints. Then the original performance function of each satellite agent was modified by introducing both the constraint punishing operator and the multi-satellite joint punishing operator. Next, a multi-satellite reinforcement learning algorithm (MUSARLA)was proposed to derive the coordinated task allocation strategy. Furethermore, the interaction among multiple satellites was designed based on blackboard architecture to reduce the communication cost while learning. Fimally, simulated experiments are carried out which verified the effectiveness of the proposed algorithm.关键词
卫星任务规划/协同规划/多智能体强化学习/黑板结构Key words
satellite mission planning/cooperative planning/multi-agent reinforcement learning/blackboard architecture分类
信息技术与安全科学引用本文复制引用
王冲,景宁,李军,王钧,陈浩..一种基于多Agent强化学习的多星协同任务规划算法[J].国防科技大学学报,2011,33(1):53-58,6.基金项目
国家自然科学基金资助项目(60604035) （60604035）
国家863高技术资助项目(2007AA12020203) （2007AA12020203）
