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offline-RL-SEEM
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2026-02-07 16:22:39
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offline-RL-SEEM SEEM: Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL Yang Yue * , Rui Lu * , Bingyi Kang * , Shiji Song , Gao Huang + ,    * Equal Contribution    + Corresponding author ▶ 1 Department of Automation, BNRist, Tsinghua University 2 Bytedance Inc. arXiv Code Slide Poster The Self-Excite Eigenvalue Measure (SEEM) offers a new theoretical framework and metric to understand, predict, and better resolve Q-value divergence in offline RL. SEEM can reliably predict upcoming divergence through the largest eigenvalue of a kernel matrix and accurately characterize the growth order of diverging Q-values. Finally, SEEM resolves divergence from a novel perspective, namely regularizing the neural network’s generalization behavior. Abstract The divergence of the Q-value estimation has been a prominent issue offline reinforcement learning (offline...
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