竞争式Takagi-Sugeno模糊再励学习

针对连续空间的复杂学习任务,提出了一种竞争式Takagi-Sugeno模糊再励学习网络(CTSFRLN),该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再励学习方法.文中相应提出了两种学习算法,即竞争式Takagi-Sugeno模糊Q-学习算法和竞争式Takagi-Sugeno模糊优胜学习算法,其把CTSFRLN训练成为一种所谓的Takagi-Sugeno模糊变结构控制器.以二级倒立摆控制系统为例,仿真研究表明所提出的学习算法在性能上优于其它的再励学习算法.

COMPETITIVE TAKAGI-SUGENO FUZZY REINFORCEMENT LEARNING

This paper proposes a competitive Takagi Sugeno fuzzy reinforcement learning network (CTSFRLN) for solving complicated learning tasks of continuous domains. The proposed CTSFRLN is constructed by combining Takagi Sugeno type fuzzy inference systems with action value based reinforcement learning methods. Two competitive learning algorithms are derived, including the competitive Takagi Sugeno fuzzy Q learning and the competitive Takagi Sugeno fuzzy advantage learning. These learning methods lead to so called Takagi Sugeno fuzzy variable structure controllers. Simulation experiments on the double inverted pendulum system demonstrate the superiority of these learning methods.