scholarly journals Safety Verification of Cyber-Physical Systems with Reinforcement Learning Control

2019 ◽  
Vol 18 (5s) ◽  
pp. 1-22 ◽  
Author(s):  
Hoang-Dung Tran ◽  
Feiyang Cai ◽  
Manzanas Lopez Diego ◽  
Patrick Musau ◽  
Taylor T. Johnson ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Control ◽  
Cyber Physical Systems ◽  
Safety Verification ◽  
Physical Systems

scholarly journals Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems

Automatica ◽  
2020 ◽  
Vol 113 ◽  
pp. 108759 ◽  
Author(s):  
Alex S. Leong ◽  
Arunselvan Ramaswamy ◽  
Daniel E. Quevedo ◽  
Holger Karl ◽  
Ling Shi
Keyword(s):  
Reinforcement Learning ◽  
Cyber Physical Systems ◽  
Wireless Sensor ◽  
Sensor Scheduling ◽  
Physical Systems

scholarly journals Safety Verification Utilizing Model-based Development for Safety Critical Cyber-Physical Systems

2017 ◽  
Vol 25 (0) ◽  
pp. 797-810 ◽  
Author(s):  
Tasuku Ishigooka ◽  
Habib Saissi ◽  
Thorsten Piper ◽  
Stefan Winter ◽  
Neeraj Suri
Keyword(s):  
Cyber Physical Systems ◽  
Safety Verification ◽  
Physical Systems ◽  
Safety Critical ◽  
Model Based

scholarly journals Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

2021 ◽  
Vol 5 (4) ◽  
pp. 1-24
Author(s):  
Jianguo Chen ◽  
Kenli Li ◽  
Keqin Li ◽  
Philip S. Yu ◽  
Zeng Zeng
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Balance ◽  
Supply And Demand ◽  
Pareto Frontier ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Optimal Dispatch ◽  
Multi Objective ◽  
Dispatching Algorithm ◽  
Multiple Dispatch

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.

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