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.

本研究解决了强化学习算法在高精度控制任务中常出现的亚优性能问题，特别是在需要达成精确目标状态的任务中。提出的顾问-演员-评论家(AAC)方法结合了反馈控制理论的精确性与强化学习的自适应学习能力，通过顾问指导演员优化控制行为，从而提高目标实现的精度。基准测试表明，AAC在需要高精度的目标导向任务中超越了标准强化学习算法，展现了其高精度、可靠性和鲁棒性。

顾问-演员-评论家: 消除强化学习控制中的稳态误差