# Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases

arXiv: https://arxiv.org/abs/2403.10112 · HTML: https://mlsanity.com/paper/2403.10112/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 78 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [The State of Sparse Training in Deep Reinforcement Learning](https://mlsanity.com/paper/2206.10369/) (2022)
- [Dynamic Sparse Training for Deep Reinforcement Learning](https://mlsanity.com/paper/2106.04217/) (2021)
- [Deep Multiagent Reinforcement Learning: Challenges and Directions](https://mlsanity.com/paper/2106.15691/) (2021)
- [Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari](https://mlsanity.com/paper/1802.08842/) (2018)
- [Internet of Things: An Overview](https://mlsanity.com/paper/1703.06409/) (2017)
- [Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2303.00912/) (2023)
- [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)
- [Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02685/) (2020)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Active Sensing for Communications by Learning](https://mlsanity.com/paper/2112.04075/) (2021)
- [The Test of Tests: A Framework For Differentially Private Hypothesis Testing](https://mlsanity.com/paper/2302.04260/) (2023)
- [Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2006.01044/) (2020)
- [Bandit Quickest Changepoint Detection](https://mlsanity.com/paper/2107.10492/) (2021)
- [Active Sensing for Localization with Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2310.13160/) (2023)
- [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)

## Built on by (most cited first, 1 of 1)

- [RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization](https://mlsanity.com/paper/2512.18788/) (2025)

## Abstract (arXiv)

Active hypothesis testing is a thoroughly studied problem that finds numerous applications in wireless communications and sensor networks. In this paper, we focus on one centralized and one decentralized problem of active hypothesis testing in the presence of an eavesdropper. For the centralized problem including a single legitimate agent, we present a new framework based on deep NeuroEvolution (NE), whereas, for the decentralized problem, we develop a novel NE-based method for solving collaborative multi-agent tasks, which, interestingly, maintains all computational benefits of our single-agent NE-based scheme. To further reduce the computational complexity of the latter scheme, a novel multi-agent joint NE and pruning framework is also designed. The superiority of the proposed NE-based evasive active hypothesis testing schemes over conventional active hypothesis testing policies, as well as learning-based methods, is validated through extensive numerical investigations in an example use case of anomaly detection over wireless sensor networks. It is demonstrated that the proposed joint optimization and pruning framework achieves nearly identical performance with its unpruned counterpart, while removing a very large percentage of redundant deep neural network weights.