研究人员引入了Q-MMR，一个用于马尔可夫决策过程（MDP）中离轨策略评估的新理论框架。该方法学习数据点的权重，以利用矩匹配目标来近似目标策略下的预期回报。一项关键发现是，对于一般的函数逼近，存在一个数据依赖的、无维度的有限样本保证，其显著特点是不依赖于函数类的复杂度。 AI
影响 引入了一个新颖的离轨策略评估理论框架，可能改进强化学习代理的训练。
排序理由 该集群包含一篇详细介绍机器学习问题新理论框架的学术论文。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2605.06474v1 Announce Type: new Abstract: We present a novel theoretical framework, Q-MMR, for off-policy evaluation in finite-horizon MDPs. Q-MMR learns a set of scalar weights, one for each data point, such that the reweighted rewards approximate the expected return under…
We present a novel theoretical framework, Q-MMR, for off-policy evaluation in finite-horizon MDPs. Q-MMR learns a set of scalar weights, one for each data point, such that the reweighted rewards approximate the expected return under the target policy. The weights are learned indu…
We present a novel theoretical framework, Q-MMR, for off-policy evaluation in finite-horizon MDPs. Q-MMR learns a set of scalar weights, one for each data point, such that the reweighted rewards approximate the expected return under the target policy. The weights are learned indu…
We present a novel theoretical framework, Q-MMR, for off-policy evaluation in finite-horizon MDPs. Q-MMR learns a set of scalar weights, one for each data point, such that the reweighted rewards approximate the expected return under the target policy. The weights are learned indu…