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Sampled-data control through model-free reinforcement learning with effective experience replay

Journal of Automation and Intelligence 2023年第1期2卷 20-30页

作者： Bo Xiao H.K.Lam Xiaojie Su Ziwei Wang Frank P.-W.Lo Shihong Chen Eric Yeatman Department of Electrical and Electronic Engineering Imperial College LondonLondon SW72AZUK Department of Engineering King’s College LondonLondon WC2B 4BGUK Department of Automation Chongqing UniversityShapingba DistrictChong QingChina School of Engineering Lancaster UniversityLA14YWUK Hamlyn Centre Imperial College LondonLondon SW72AZUK

Reinforcement Learning(RL)based control algorithms can learn the control strategies for nonlinear and uncertain environment during interacting with *** by the rewards generated by environment,a RL agent can learn the control strategy directly in a model-free way instead of investigating the dynamic model of the *** the paper,we propose the sampled-data RL control strategy to reduce the computational *** the sampled-data control strategy,the whole control system is of a hybrid structure,in which the plant is of continuous structure while the controller(RL agent)adopts a discrete *** that the continuous states of the plant will be the input of the agent,the state–action value function is approximated by the fully connected feed-forward neural networks(FCFFNN).Instead of learning the controller at every step during the interaction with the environment,the learning and acting stages are decoupled to learn the control strategy more effectively through experience *** the acting stage,the most effective experience obtained during the interaction with the environment will be stored and during the learning stage,the stored experience will be replayed to customized times,which helps enhance the experience replay *** effectiveness of proposed approach will be verified by simulation examples.

关键词： Reinforcement learning Neural networks Sampled-data control Model-free effective experience replay

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