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arXiv · 2102.08571

Self-Triggered Markov Decision Processes

Yunhan Huang·Quanyan Zhu
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Abstract

In this paper, we study Markov Decision Processes (MDPs) with self-triggered strategies, where the idea of self-triggered control is extended to more generic MDP models. This extension broadens the application of self-triggering policies to a broader range of systems. We study the co-design problems of the control policy and the triggering policy to optimize two pre-specified cost criteria. The first cost criterion is introduced by incorporating a pre-specified update penalty into the traditional MDP cost criteria to reduce the use of communication resources. Under this criteria, a novel dynamic programming (DP) equation called DP equation with optimized lookahead to proposed to solve for the self-triggering policy under this criteria. The second self-triggering policy is to maximize the triggering time while still guaranteeing a pre-specified level of sub-optimality. Theoretical underpinnings are established for the computation and implementation of both policies. Through a gridworld numerical example, we illustrate the two policies' effectiveness in reducing sources consumption and demonstrate the trade-offs between resource consumption and system performance.

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Yunhan Huang, Quanyan Zhu. 2021-02-17. Self-Triggered Markov Decision Processes. https://arxiv.org/abs/2102.08571

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