控制理论与应用2011，Vol.28Issue(11)：1595-1600,1606,7.
基于概率模型的动态分层强化学习
Dynamic hierarchical reinforcement learning based on probability model
摘要
Abstract
To deal with the overwhelming dimensionality in the large-scale reinforcement-learning and the strong depen-dence on prior knowledge in existing learning algorithms,we propose the method of dynamic hierarchical reinforcement learning based on the probability model（DHRL--model）.This method identifies some key states automatically based on probability parameters of the state-transition probability model established based on Bayesian learning,then generates some state-subspaces dynamically by clustering,and learns the optimal policy based on hierarchical structure.Simulation results show that DHRL--model algorithm improves the learning efficiency of the agent remarkably in the complex environment,and can be applied to learning in the unknown large-scale world.关键词
动态分层强化学习/贝叶斯学习/状态转移概率模型/智能体Key words
dynamic hierarchical reinforcement-learning/Bayesian learning/state-transition probability model/agent分类
信息技术与安全科学引用本文复制引用
戴朝晖,袁姣红,吴敏,陈鑫..基于概率模型的动态分层强化学习[J].控制理论与应用,2011,28(11):1595-1600,1606,7.基金项目
国家自然科学基金资助项目 （）
中国博士后科学基金一等资助项目 （）
中国博士后科学基金特别资助项目 （）
教育部高等学校博士点基金新教师基金资助项目 （）
