# Policy Design for Active Sequential Hypothesis Testing using Deep Learning

arXiv: https://arxiv.org/abs/1810.04859 · HTML: https://mlsanity.com/paper/1810.04859/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary](https://mlsanity.com/paper/2102.08308/) (2021)
- [Smoother Entropy for Active State Trajectory Estimation and Obfuscation in POMDPs](https://mlsanity.com/paper/2108.10227/) (2021)
- [Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing](https://mlsanity.com/paper/2112.04912/) (2021)
- [Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning](https://mlsanity.com/paper/2303.10623/) (2023)
- [Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing](https://mlsanity.com/paper/2309.08477/) (2023)
- [A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing](https://mlsanity.com/paper/2105.06289/) (2021)
- [Anomaly Detection via Controlled Sensing and Deep Active Inference](https://mlsanity.com/paper/2105.06288/) (2021)
- [Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing](https://mlsanity.com/paper/2201.00879/) (2022)
- [Anomaly Detection via Learning-Based Sequential Controlled Sensing](https://mlsanity.com/paper/2312.00088/) (2023)
- [TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance](https://mlsanity.com/paper/2312.04597/) (2023)
- [Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases](https://mlsanity.com/paper/2403.10112/) (2024)
- [Searching for a Hidden Markov Anomaly over Multiple Processes](https://mlsanity.com/paper/2506.17108/) (2025)
- [Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems](https://mlsanity.com/paper/2507.00268/) (2025)

## Abstract (arXiv)

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.