研究人员推出了一种新颖的离线强化学习占用率估计方法——拟合占用率评估（FORE）。该技术通过伴随贝尔曼递归来表征折扣占用率，并在每次迭代中解决密度比目标。与需要贝尔曼完备性的先前方法不同，FORE的核心近似条件是折扣占用率本身的实现性，从而提供了收敛保证和有限样本界限。 AI
影响 引入了离线强化学习的新理论框架，可能提高模型评估的准确性。
排序理由 该集群包含一篇详细介绍强化学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.05375v1 Announce Type: new Abstract: Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation. Existing primal-dual and minimax methods typically estimate these ratios by enforcing occupancy-balance moments …
Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation. Existing primal-dual and minimax methods typically estimate these ratios by enforcing occupancy-balance moments over a critic class. We propose fitted occupancy…