NDLI: Gittins index based control policy for a class of pursuit-evasion problems

Content Provider	IET Digital Library
Author	Tan, Cheng Xu, Changbao Yang, Lin Wong, Wing Shing
Abstract	In this study, the authors develop a novel approach to a class of pursuit-evasion problems modelled in the form of discrete time feedback control systems, where the opposing parties have asymmetric capability. The authors assume the control policy of the evader, described as a random variable, is unknown to the pursuer. This pursuit-evasion problem is formulated as a quadratic optimisation problem from the perspective of the pursuer. Due to the curse of dimensionality, this pursuit-evasion problem cannot be practically solved by dynamic programming. In this study, the authors reformulate it as a multi-armed bandit problem. A heuristic policy based on the Gittins index is proposed to solve this problem, which can be computed based on a forward induction. Simulation results show the proposed policy outperforms a random decision policy.
Starting Page	110
Ending Page	118
Page Count	9
ISSN	17518644
Volume Number	12
e-ISSN	17518652
Issue Number	Issue 1, Jan (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/12/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2017.0398
Journal	IET Control Theory & Applications
Publisher Date	2017-10-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Asymmetric Capability Discrete Control System Discrete Time Feedback Control System Discrete Time System Evader Control Policy Feedback Forward Induction Gittins Index Based Control Policy Heuristic Policy Optimisation Technique Probability Pursuit-evasion Problem Quadratic Optimisation Problem Quadratic Programming Random Decision Policy Random Variable Statistics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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