Information theory has been very successful in obtaining performance limits
for various problems such as communication, compression and hypothesis testing.
Likewise, stochastic control theory provides a characterization of optimal
policies for Partially Observable Markov Decision Processes (POMDPs) using
dynamic programming. However, finding optimal policies for these problems is
computationally hard in general and thus, heuristic solutions are employed in
practice. Deep learning can be used as a tool for designing better heuristics
in such problems. In this paper, the problem of active sequential hypothesis
testing is considered. The goal is to design a policy that can reliably infer
the true hypothesis using as few samples as possible by adaptively selecting
appropriate queries. This problem can be modeled as a POMDP and bounds on its
value function exist in literature. However, optimal policies have not been
identified and various heuristics are used. In this paper, two new heuristics
are proposed: one based on deep reinforcement learning and another based on a
KL-divergence zero-sum game. These heuristics are compared with
state-of-the-art solutions and it is demonstrated using numerical experiments
that the proposed heuristics can achieve significantly better performance than
existing methods in some scenarios.

该研究使用基于深度学习的启发式和 KL 分歧零和游戏设计方法来解决部分可观测马尔可夫决策过程中的主动序贯假设测试问题，并表明在某些情况下所提出的启发式方法优于现有方法。