# Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning

arXiv: https://arxiv.org/abs/2303.10623 · HTML: https://mlsanity.com/paper/2303.10623/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs](https://mlsanity.com/paper/2110.05038/) (2021)
- [Gated Feedback Recurrent Neural Networks](https://mlsanity.com/paper/1502.02367/) (2015)
- [Policy Design for Active Sequential Hypothesis Testing using Deep Learning](https://mlsanity.com/paper/1810.04859/) (2018)
- [Deep Actor-Critic Reinforcement Learning for Anomaly Detection](https://mlsanity.com/paper/1908.10755/) (2019)
- [Federated TON_IoT Windows Datasets for Evaluating AI-based Security Applications](https://mlsanity.com/paper/2010.08522/) (2020)
- [Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing](https://mlsanity.com/paper/2112.04912/) (2021)

## Built on by (most cited first, 4 of 4)

- [Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach](https://mlsanity.com/paper/2510.13819/) (2025)
- [Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases](https://mlsanity.com/paper/2403.10112/) (2024)
- [Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments](https://mlsanity.com/paper/2407.04974/) (2024)
- [Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach](https://mlsanity.com/paper/2607.00056/) (2026)

## Abstract (arXiv)

A combination of deep reinforcement learning and supervised learning is proposed for the problem of active sequential hypothesis testing in completely unknown environments. We make no assumptions about the prior probability, the action and observation sets, and the observation generating process. Our method can be used in any environment even if it has continuous observations or actions, and performs competitively and sometimes better than the Chernoff test, in both finite and infinite horizon problems, despite not having access to the environment dynamics.