Title | Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding |
Author | |
Corresponding Author | Peng Yang |
Publication Years | 2022-12
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Conference Name | the 17th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2022)
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Source Title | |
Conference Date | 2022-12-16
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Conference Place | 武汉
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Abstract | Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. |
Keywords | |
SUSTech Authorship | First
; Corresponding
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Language | English
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Indexed By | |
Data Source | 人工提交
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/523892 |
Department | Department of Computer Science and Engineering 理学院_统计与数据科学系 工学院_斯发基斯可信自主研究院 |
Affiliation | 1.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Chin 2.Faculty of engineering, Shenzhen MSU-BIT university, Shenzhen 518172, Chin 3.Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen 518055, Chin 4.Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, Chin |
First Author Affilication | Department of Computer Science and Engineering |
Corresponding Author Affilication | Department of Computer Science and Engineering; Department of Statistics and Data Science |
First Author's First Affilication | Department of Computer Science and Engineering |
Recommended Citation GB/T 7714 |
Lan Tang,Xiaxi Li,Jinyuan Zhang,et al. Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding[C],2022.
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