Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Junhua, Duan | Yi-an, Zhu; * | Dong, Zhong | Lixiang, Zhang | Lin, Zhang
Affiliations: School of Computer, Northwestern Polytechnical University, Beilin District, Xi’an Shaanxi, P.R. China
Correspondence: [*] Corresponding author. Zhu Yi-an, School of Computer, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an Shaanxi, 710072, P.R. China. E-mail: zhuya@nwpu.edu.cn.
Abstract: Transfer learning has been identified as conducive to improving the speed of machine learning in many areas. In multi-task reinforcement learning, transfer learning can assist the transfer of experiences between different tasks. The research conducted in this article is focused on two aspects. On the one hand, multi-task parallel transfer learning can improve the learning speed of parallel learning tasks. On the other hand, the learning of the current optimal experience can help the target point rewards to be transmitted to the starting point. The value of this self-learning can also accelerate the convergence speed of the reinforcement learning. According to the research into these two aspects, this paper uses the idea of particle swarm optimization (PSO) to conduct self-learning and interactive learning in multi-task parallel learning. In this paper, a new multi-task learning algorithm named PSO-MTPRL (Multi-Task Parallel Reinforcement Learning based on PSO) is proposed. Based on the idea of PSO algorithm, the Boltzmann strategy, Self-Learning Process (SLP) and Interactive Learning Process (ILP) are selected probabilistically. Based on the characteristic exhibited by reinforcement learning, segmented learning model is recommended. In the early learning stages, the complete Boltzmann exploration strategy is applied, and B-SLP-ILP (Boltzmann-SLP- ILP) learning procedure is conducted exclusively in the middle stage of the learning. In the late learning stages, Boltzmann exploration is involved again. The segmented learning model can help ensure the balance of the exploration and exploitation, in addition to ensuring that all tasks convergence.
Keywords: Multi-task reinforcement learning, parallel reinforcement learning, particle swarm optimization, transfer learning
DOI: 10.3233/JIFS-190209
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 8567-8575, 2019
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl