Deadlock-Detection via Reinforcement LearningChen M* and Rabelo L
Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, USA
- *Corresponding Author:
- Mengmeng Chen
Department of Industrial Engineering and Management Systems
University of Central Florida, Orlando, USA
Tel: 407 823-2204
E-mail: [email protected]
Received Date: May 02, 2017; Accepted Date: June 01, 2017; Published Date: June 16, 2017
Citation: Chen M, Rabelo L (2017) Deadlock-Detection via Reinforcement Learning. Ind Eng Manage 6: 215. doi:10.4172/2169-0316.1000215
Copyright: © 2017 Chen M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Optimization of makespan in scheduling is a highly desirable research topic, deadlock detection and prevention is one of the fundamental issues. Supported by what learned from this class, a reinforcement learning approach is developed to unravel this optimization difficulty. By evaluating this RL model on forty classical non-buffer benchmarks and compare with other alternative algorithms, we presented a near-optimal result.