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Abstract <p>This paper presents an innovative approach to overcoming the barrier of transferring reinforcement learning policies between different physical simulators (Sim2Sim). We propose the Action Correction Network (ACN) architecture, a two-component neural network that corrects policy actions taking into account differences in simulator dynamics. The effectiveness of the method is experimentally demonstrated using the example of transferring the walking policy for the Unitree A1 quadruped robot between the PyBullet and MuJoCo simulators.</p>

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Multilayer Action Correction Perceptron for Overcoming the Barrier between Simulation and the Real World when Training Quadrupedal Robot Policies

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • A. S. Geroyev,
  • O. M. Gerget

摘要

Abstract

This paper presents an innovative approach to overcoming the barrier of transferring reinforcement learning policies between different physical simulators (Sim2Sim). We propose the Action Correction Network (ACN) architecture, a two-component neural network that corrects policy actions taking into account differences in simulator dynamics. The effectiveness of the method is experimentally demonstrated using the example of transferring the walking policy for the Unitree A1 quadruped robot between the PyBullet and MuJoCo simulators.