基于多智能体强化学习的多机器人协作策略研究  

Research on multi-robot cooperation strategy based on multi-agent reinforcement learning

作　　者：段勇[1] 徐心和[2] 

机构地区：[1]沈阳工业大学信息科学与工程学院,沈阳110870 [2]东北大学信息科学与工程学院,沈阳110004

出　　处：《系统工程理论与实践》2014年第5期1305-1310,共6页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(60905054);辽宁省高等学校杰出青年学者成长计划(LJQ2011006)

摘　　要：研究了一种基于智能体动作预测的多智能体强化学习算法.在多智能体系统中,学习智能体选择动作不可避免地要受到其他智能体执行动作的影响,因此强化学习系统需要考虑多智能体的联合状态和联合动作.基于此,提出使用概率神经网络来预测其他智能体动作的方法,从而构成多智能体联合动作,实现了多智能体强化学习算法.此外,研究了该方法在足球机器人协作策略学习中的应用,使多机器人系统能够通过与环境的交互学习来掌握行为策略,从而实现多机器人的分工和协作.A multi-agent reinforcement learning method based on action prediction of other agents is proposed. In the multi-agent system, the action selection of a learning agent must be effected by other agents＇ action. Therefore, joint-state and joint-action are involved in reinforcement learning system. The method of agent action prediction based on the probabilistic neural network is proposed. So the joint- action is formed and multi-agent reinforcement learning is implemented. Furthermore, the application of the proposed method in cooperation strategy learning of robot soccer is studied. Through mutually learning with environment, multiple robot system can master behavior strategy and realize multiple robots coordination and cooperation.

关 键 词：多智能体系统 强化学习 概率神经网络 多机器人协作 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP181[自动化与计算机技术—控制科学与工程]