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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2635-2640

Research on deep reinforcement learning method based on improved curiosity

Qiao He
Li Zenghui
Liu Chun
Hu Sidong
School of Electrical & Control Engineering, Liaoning Technology University, Huludao Liaoning 125105, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0014

Abstract

In the deep reinforcement learning method, the intrinsic curiosity model(ICM) guides the agent to obtain the opportunity to learn unknown strategies in the sparse reward environment, but the curiosity reward is a state difference value, which will make the agent pay too much attention to the exploration of new states, then could be the problem of blind exploration arises. To solve the above problem, this paper proposed an intrinsic curiosity model algorithm based on knowledge distillation(KD-ICM). Firstly, it introduced the method of knowledge distillation to make the agent acquire more abundant environmental information and strategy knowledge in a short time and accelerate the learning process. Secondly, by pre-training teachers' neural network model to guide the forward network to obtain a forward network model with higher accuracy and performance, reduced the blind exploration of agents. It designed two different simulation experiments on the Unity simulation platform for comparison. The experiments show that in the complex simulation task environment, the average reward of KD-ICM algorithm is 136% higher than that of ICM, and the optimal action probability is 13.47% higher than that of ICM. Both the exploration performance of the agent and the exploration quality can be improved, and it verifies the feasibility of the algorithm.

Foundation Support

国家自然科学基金资助项目(51604141,51204087)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0014
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Algorithm Research & Explore
Pages: 2635-2640
Serial Number: 1001-3695(2024)09-010-2635-06

Publish History

[2024-05-11] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

乔和, 李增辉, 刘春, 等. 基于改进好奇心的深度强化学习方法 [J]. 计算机应用研究, 2024, 41 (9): 2635-2640. (Qiao He, Li Zenghui, Liu Chun, et al. Research on deep reinforcement learning method based on improved curiosity [J]. Application Research of Computers, 2024, 41 (9): 2635-2640. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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