文章基本信息

标题：Deep Reinforcement Learning for Continuous-time Self-triggered Control ⁎
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作者：Ran Wang ; Ibuki Takeuchi ; Kenji Kashima 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：14
页码：203-208
DOI：10.1016/j.ifacol.2021.10.353
语种：English
出版社：Elsevier
摘要：AbstractIn recent years, the trade-off between communication cost and control performance has become increasingly important. Among various control architectures, self-triggered controllers decide the next communication (state observation and action determination) timing online in a state-dependent manner. However, it should be emphasized that most of the existing methods do not explicitly evaluate the resulting long-run communication cost. In this paper, we formulate an optimal continuous-time self-triggered control problem that takes the communication cost into an explicit account and proposes a design method based on deep reinforcement learning.
关键词：KeywordsSelf-triggered controldata-driven control system designmachine learning