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arXiv · 2502.02265

Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control

Donghe Chen·Yubin Peng·Tengjie Zheng·Han Wang·Chaoran Qu·Lin Cheng
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Abstract

High-precision control tasks present substantial challenges for reinforcement learning (RL) algorithms, frequently resulting in suboptimal performance attributed to network approximation inaccuracies and inadequate sample quality.These issues are exacerbated when the task requires the agent to achieve a precise goal state, as is common in robotics and other real-world applications.We introduce Adviser-Actor-Critic (AAC), designed to address the precision control dilemma by combining the precision of feedback control theory with the adaptive learning capability of RL and featuring an Adviser that mentors the actor to refine control actions, thereby enhancing the precision of goal attainment.Finally, through benchmark tests, AAC outperformed standard RL algorithms in precision-critical, goal-conditioned tasks, demonstrating AAC's high precision, reliability, and robustness.Code are available at: https://anonymous.4open.science/r/Adviser-Actor-Critic-8AC5.

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Donghe Chen, Yubin Peng, Tengjie Zheng, Han Wang, Chaoran Qu, Lin Cheng. 2025-02-04. Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control. https://arxiv.org/abs/2502.02265

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