Name
Title | ||
|---|---|---|
Efficiently Learning A Distributed Control Policy In Cyber-Physical Production Systems Via Simulation Optimization |
Abstract | ||
|---|---|---|
The manufacturing industry is becoming more dynamic than ever. The limitations of non-deterministic network delays and real-time requirements call for decentralized control. For such dynamic and complex systems, learning methods stand out as a transformational technology to have a more flexible control solution. Using simulation for learning enables the description of highly dynamic systems and provides samples without occupying a real facility. However, it requires prohibitively expensive computation. In this paper, we argue that simulation optimization is a powerful tool that can be applied to various simulation-based learning processes for tremendous effects. We proposed an efficient policy learning framework, ROSA (Reinforcement-learning enhanced by Optimal Simulation Allocation), with unprecedented integration of learning, control, and simulation optimization techniques, which can drastically improve the efficiency of policy learning in a cyber-physical system. A proof-of-concept is implemented on a conveyer-switch network, demonstrating how ROSA can be applied for efficient policy learning, with an emphasis on the industrial distributed control system. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CASE48305.2020.9249228 | 2020 IEEE 16TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) |
Keywords | DocType | ISSN |
distributed control, cyber-physical system, simulation optimization, reinforcement learning, multi-agent | Conference | 2161-8070 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minjie Zou | 1 | 2 | 2.08 |
| Edward Huang | 2 | 64 | 7.87 |
| Vogel-Heuser, B. | 3 | 521 | 125.47 |
| Chun-Hung Cherr | 4 | 0 | 0.34 |