A deep reinforcement learning method to control chaos synchronization between two identical chaotic systems

We propose a model-free deep reinforcement learning method for controlling the synchronization between two identical chaotic systems, one target and one reference. By interacting with the target and the reference, the agent continuously optimizes its strategy of applying perturbations to the target...

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Bibliographic Details
Published in:Chaos, solitons and fractals Vol. 174; p. 113809
Main Authors: Cheng, Haoxin, Li, Haihong, Dai, Qionglin, Yang, Junzhong
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2023
Subjects:
Chaos synchronization
Continuous control
Deep reinforcement learning
Model-free method
Deep reinforcement learning
Chaos synchronization
Model-free method
Continuous control
ISSN:0960-0779
Online Access:Get full text
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Summary:We propose a model-free deep reinforcement learning method for controlling the synchronization between two identical chaotic systems, one target and one reference. By interacting with the target and the reference, the agent continuously optimizes its strategy of applying perturbations to the target to synchronize the trajectory of the target with the reference. This method is different from previous chaos synchronization methods. It requires no prior knowledge of the chaotic systems. We apply the deep reinforcement learning method to several typical chaotic systems (Lorenz system, Rössler system, Chua circuit and Logistic map) and its efficiency of controlling synchronization between the target and the reference is demonstrated. Especially, we find that a single learned agent can be used to control the chaos synchronization for different chaotic systems. We also find that the method works well in controlling chaos synchronization even when only incomplete information of the state variables of the target and the reference can be obtained. •A model-free deep reinforcement learning method for controlling chaos synchronization is proposed.•The efficiency of controlling synchronization is demonstrated.•A single learned agent can be used to control the chaos synchronization for different chaotic systems.•The method works well even when only incomplete information of the state variables of the chaotic systems can be obtained.
ISSN:0960-0779
DOI:10.1016/j.chaos.2023.113809