Value-Guided Repulsion for Offline Reinforcement Learning· 值引导的离线强化学习排斥机制

Drifting models have emerged as a highly efficient and stable paradigm for policy design in offline reinforcement learning (RL). However, standard drift policies determine repulsion solely from geometric relationships among generated actions and therefore cannot distinguish them by expected value. To address this limitation, we introduce Value-Guided Repulsion Q-Learning (VRQL) to integrate action value estimates into the particle interaction field. For each state, VRQL combines geometric kernel distance with a normalized Q value scaling. This mechanism assigns greater repulsive influence to particles with lower values while strictly retaining attraction toward behavior action. Consequently, this value-guided interaction field aligns behavior regularization with policy improvement without