The Application of Residual Connection-Based State Normalization Method in GAIL

Yanning Ge, Tao Huang, Xiaoding Wang, Guolong Zheng () and Xu Yang ()
Additional contact information
Yanning Ge: College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
Tao Huang: College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
Xiaoding Wang: College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
Guolong Zheng: College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
Xu Yang: College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China

Mathematics, 2024, vol. 12, issue 2, 1-14

Abstract: In the domain of reinforcement learning (RL), deriving efficacious state representations and maintaining algorithmic stability are crucial for optimal agent performance. However, the inherent dynamism of state representations often complicates the normalization procedure. To overcome these challenges, we present an innovative RL framework that integrates state normalization techniques with residual connections and incorporates attention mechanisms into generative adversarial imitation learning (GAIL). This combination not only enhances the expressive capability of state representations, thereby improving the agent’s accuracy in state recognition, but also significantly mitigates the common issues of gradient dissipation and explosion. Compared to traditional RL algorithms, GAIL combined with the residual connection-based state normalization method empowers the agent to markedly reduce the exploration duration such that feedback concerning rewards in the current state can be provided in real time. Empirical evaluations demonstrate the superior efficacy of this methodology across various RL environments.

Keywords: reinforcement learning; generative adversarial imitation learning (GAIL); residual connection; state normalization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/2/214/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/2/214/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:2:p:214-:d:1315647

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().