摘要
在基于半自治agent的系统中应用profit-sharing增强学习方法,并与基于动态规划的Q-learning增强学习方法进行比较,在不确定因素较多的动态环境中,当系统状态变化不是一个马尔科夫过程时profit-sharing方法具有很大优势。根据半自治agent中半自治的特性——受制性,提出了一种面向基于半自治agent的增强学习模型,以战场仿真中安全隐蔽的寻找模型为实例对基于半自治agent的profit-sharing增强学习模型进行了试验分析。
We exert the profit-sharing reinforcement learning method into the semi-autonomous agent system,and compare it with the other reinforce learning method Q-learning.Profit-sharing method is more robust and fit for the dynamic environment which includes many uncertain factors,especially in the partial MDPs（Markov Decision Processes） environment.Facing the semi - autonomous property of the agent,we propose an improving learning method of profit-sharing in the semi-autonomous agent system and test it in a combat simulation environment that finds the safety hidden space in battlefield.At last we contract and analyze these methods to the others.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第15期72-75,97,共5页
Computer Engineering and Applications
基金
国家部委"十五"预研项目(the Pre- Research Project of the "Tenth Five- Year- Plan"of China) 。
