Algorithm Research & Explore
|
1699-1703

Transfer reinforcement learning algorithm with double Q-learning

Zeng Ruia,b
Zhou Jianb,c
Liu Manlub,c
Zhang Junjuna,b
Chen Zhuoa,b
a. School of Manufacturing Science & Engineering, b. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, c. School of Information Engineering, Southwest University of Science & Technology, Mianyang Sichuan 621000, China

Abstract

Deep reinforcement learning explores a large number of environmental samples during the training process, which will cause the algorithm to take too long to converge. Reuse or transfer the knowledge of the previous task(source task), which has the potential to improve the convergence speed for the learning of the algorithm in the new task(target task). In order to improve the efficiency of algorithm learning, this paper proposed transfer reinforcement learning algorithm with double Q-lear-ning. The algorithm based on the actor-critic framework utilized the knowledge of the optimal value function of the source task, so that the value function network of the target task made a more accurate evaluation of the strategy, and guided the stra-tegy to quickly update in the direction of the optimal strategy. In Open AI Gym and the experiments where manipulator reaches the target position in the three-dimensional space, this algorithm achieves better results than conventional deep reinforcement learning algorithms. Experiments show that transfer reinforcement learning algorithm with double Q-learning has faster convergence speed, and the algorithm exploration is more stable during the training process.

Foundation Support

国家“十三五”核能开发项目(20161295)
国家科技重大专项资助项目(2019ZX06002022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.09.0232
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 6
Section: Algorithm Research & Explore
Pages: 1699-1703
Serial Number: 1001-3695(2021)06-018-1699-05

Publish History

[2021-06-05] Printed Article

Cite This Article

曾睿, 周建, 刘满禄, 等. 双Q网络学习的迁移强化学习算法 [J]. 计算机应用研究, 2021, 38 (6): 1699-1703. (Zeng Rui, Zhou Jian, Liu Manlu, et al. Transfer reinforcement learning algorithm with double Q-learning [J]. Application Research of Computers, 2021, 38 (6): 1699-1703. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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