趋同(经济学)
基础(拓扑)
模糊规则
模糊逻辑
参数化复杂度
强化学习
计算机科学
产品(数学)
数学优化
人工智能
数学
模糊集
算法
数学分析
几何学
经济
经济增长
作者
H.R. Berenji,David Vengerov
标识
DOI:10.1109/fuzz.2001.1009030
摘要
This paper provides the first convergence proof for fuzzy reinforcement learning. We extend the work of Konda and Tsitsiklis (2000), who presented a convergent actor-critic algorithm for a general parameterized actor. In our work we prove that a fuzzy rule base actor satisfies the necessary conditions that guarantee the convergence of its parameters to a local optimum. Our fuzzy rule base uses the Takagi-Sugeno-Kang rules, Gaussian membership functions and product inference.
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