文章基本信息

标题：Dual MPC with Reinforcement Learning
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作者：Juan E. Morinelly ; B. Erik Ydstie
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：7
页码：266-271
DOI：10.1016/j.ifacol.2016.07.276
语种：English
出版社：Elsevier
摘要：An adaptive optimal control algorithm for systems with uncertain dynamics is formulated under a Reinforcement Learning framework. An embedded exploratory component is included explicitly in the objective function of an output feedback receding horizon Model Predictive Control problem. The optimization is formulated as a Quadratically Constrained Quadratic Program and it is solved to e-global optimality. The iterative interaction between the action specified by the optimal solution and the approximation of cost functions balances the exploitation of current knowledge and the need for exploration. The proposed method is shown to converge to the optimal policy for a controllable discrete time linear plant with unknown output parameters.
关键词：Adaptive controldual controloptimal controlmodel predictive controlreinforcement learningapproximate dynamic programming