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This study explores the integration of the Kolmogorov-Arnold Network (KAN) model into continuous reinforcement learning (CRL). We incorporate KAN and its improved variant, MultKAN, into the on-policy PPO algorithm, resulting in a new approach called KAN-PPO. Using the Mujoco Half-cheetah environment, we conduct extensive experiments to compare the performance and generalization of KAN-PPO models against MLP-PPO structures. Additionally, we examine the effects of dropout regularization through the DropKAN method in dense network settings. Our findings demonstrate the effectiveness of KAN and MultKAN models in CRL, highlighting their potential for complex learning tasks.

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KAN-Based On-Policy PPO Method for Continuous Reinforcement Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Ali Bayeh,
  • Malek Mouhoub,
  • Samira Sadaoui

摘要

This study explores the integration of the Kolmogorov-Arnold Network (KAN) model into continuous reinforcement learning (CRL). We incorporate KAN and its improved variant, MultKAN, into the on-policy PPO algorithm, resulting in a new approach called KAN-PPO. Using the Mujoco Half-cheetah environment, we conduct extensive experiments to compare the performance and generalization of KAN-PPO models against MLP-PPO structures. Additionally, we examine the effects of dropout regularization through the DropKAN method in dense network settings. Our findings demonstrate the effectiveness of KAN and MultKAN models in CRL, highlighting their potential for complex learning tasks.