摘要
主要研究了强化学习算法及其在机器人足球比赛技术动作学习问题中的应用.强化学习的状态空间和动作空间过大或变量连续,往往导致学习的速度过慢甚至难于收敛.针对这一问题,提出了基于T-S模型模糊神经网络的强化学习方法,能够有效地实现强化学习状态空间到动作空间的映射.此外,使用提出的强化学习方法设计了足球机器人的技术动作,研究了在不需要专家知识和环境模型情况下机器人的行为学习问题.最后,通过实验证明了所研究方法的有效性,其能够满足机器人足球比赛的需要.
This paper discusses reinforcement learning （RL） algorithm and its application to technical action learning of soccer robot. In RL, since the state space and action space are too large or their variables are continuous, the learning speed are too slow and it is usually too hard for learning to converge. To solve this problem, an RL method based on T-S model fuzzy neural network is proposed, which can effectively perform the mapping from the state space to the action space of RL. Furthermore, the proposed method is used to design technical actions of soccer robot, and behavior learning of the robot without expert knowledge and environment model is discussed. Finally, experiments are made and the results show that the presented method is effective and it can meet the demands of robot soccer match.
出处
《机器人》
EI
CSCD
北大核心
2008年第5期453-459,共7页
Robot
基金
国家自然科学基金(60475036)
关键词
强化学习
机器人足球比赛
行为学习
T-S模糊神经网络
reinforcement learning （RL）
robot soccer match
behavior learning
T-S fuzzy neural network
