Title: Multi-threading parallel reinforcement learning

Authors: Qiming Fu; Yiyi Kang; Zhen Gao; Hongjie Wu; Fuyuan Hu; Jianping Chen; Shan Zhong

Addresses: Institute of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China; Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, Jiangsu, China ' Institute of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China; Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, Jiangsu, China ' Institute of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China; Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, Jiangsu, China ' Institute of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China; Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, Jiangsu, China ' Institute of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China; Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, Jiangsu, China ' Institute of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China; Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, Jiangsu, China ' Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China; School of Computer Science and Technology, Changshu Institute of Technology, Suzhou 215500, Jiangsu, China

Abstract: With respect to the problem of the slow convergence of the traditional reinforcement learning algorithm in practical applications, we propose a novel multi-threading parallel reinforcement learning algorithm - MPRL. MPRL is mainly composed of two parts. One is the FCM-based reinforcement learning multi-threading partitioning method, which transforms the multi-threading partitioning problem into a clustering partition problem to obtain the optimal multi-threading partitioning solution. The other is the parallel reinforcement learning framework, which makes the parallel execution between the policy evaluation and the interaction with the environment. In the learning process, the experience replay is adopted to update the value function, which can also solve the problem of the non-convergence in the off-policy evaluation. Experimentally, the MPRL algorithm is applied to the windy grid world problem and the cart pole problem, and compared with Q-Learning, Sarsa and KCACL. The experimental results show that MPRL has a faster convergence rate and better convergence performance.

Keywords: reinforcement learning; multi-threading technology; thread partitioning; parallel reinforcement learning; experience replay.

DOI: 10.1504/IJCAT.2019.103305

International Journal of Computer Applications in Technology, 2019 Vol.61 No.4, pp.278 - 286

Received: 05 Jan 2019
Accepted: 07 Mar 2019
Published online: 25 Oct 2019 *

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