Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding

Tang，Lan1; Li，Xiaxi2; Zhang，Jinyuan1; Li，Guiying1,3; Yang，Peng1,4; Tang，Ke1,3

10.1007/978-981-99-1549-1_19

2023

1865-0929

1865-0937

Communications in Computer and Information Science

1801 CCIS

233-247

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.

Evolutionary algorithms Reinforcement learning Surrogates

First ; Corresponding

English

2-s2.0-85161399809

Scopus

1.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Faculty of Engineering,Shenzhen MSU-BIT University,Shenzhen,518172,China
3.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
4.Department of Statistics and Data Science,Southern University of Science and Technology,Shenzhen,518055,China

Tang，Lan,Li，Xiaxi,Zhang，Jinyuan,et al. Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding[C],2023:233-247.