This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case. Motivated by real-world settings such as loading in game playing, we propose an enhanced feedback model called ``multiple observations in hindsight'', where after each episode of interaction with the POMDP, the learner may collect multiple additional observations emitted from the encountered latent states, but may not observe the latent states themselves. We show that sample-efficient learning under this feedback model is possible for two new subclasses of POMDPs: \emph{multi-observation revealing POMDPs} and \emph{distinguishable POMDPs}. Both subclasses generalize and substantially relax \emph{revealing POMDPs} -- a widely studied subclass for which sample-efficient learning is possible under standard trajectory feedback. Notably, distinguishable POMDPs only require the emission distributions from different latent states to be \emph{different} instead of \emph{linearly independent} as required in revealing POMDPs.

该研究针对学习中的部分可观察马尔可夫决策过程的样本效率进行了研究，并提出了一种增强的反馈模型，可在后见中收集多个额外观察量来实现样本高效学习。该模型适用于两个新的POMDP子类：多观察揭示POMDP和可区分POMDP，同时这两个子类也放宽了传统的揭示POMDP的要求。

多观测视角下的样本高效学习POMDPs