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

Author

Listed:
  • Yanning Ge

    (College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
    These authors contributed equally to this work.)

  • Tao Huang

    (College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Yanning Ge & Tao Huang & Xiaoding Wang & Guolong Zheng & Xu Yang, 2024. "The Application of Residual Connection-Based State Normalization Method in GAIL," Mathematics, MDPI, vol. 12(2), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:214-:d:1315647
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    References listed on IDEAS

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    1. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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