A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 4
Jul.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Teng Liu, Bin Tian, Yunfeng Ai, Li Li, Dongpu Cao and Fei-Yue Wang, "Parallel Reinforcement Learning: A Framework and Case Study," IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp. 827-835, July 2018. doi: 10.1109/JAS.2018.7511144
Citation: Teng Liu, Bin Tian, Yunfeng Ai, Li Li, Dongpu Cao and Fei-Yue Wang, "Parallel Reinforcement Learning: A Framework and Case Study," IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp. 827-835, July 2018. doi: 10.1109/JAS.2018.7511144

Parallel Reinforcement Learning: A Framework and Case Study

doi: 10.1109/JAS.2018.7511144
Funds:

the National Natural Science Foundation of China 61503380

the Natural Science Foundation of Guangdong Province, China 2015A030310187

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  • In this paper, a new machine learning framework is developed for complex system control, called parallel reinforcement learning. To overcome data deficiency of current data-driven algorithms, a parallel system is built to improve complex learning system by self-guidance. Based on the Markov chain (MC) theory, we combine the transfer learning, predictive learning, deep learning and reinforcement learning to tackle the data and action processes and to express the knowledge. Parallel reinforcement learning framework is formulated and several case studies for real-world problems are finally introduced.

     

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