# Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing

arXiv: https://arxiv.org/abs/2201.00879 · HTML: https://mlsanity.com/paper/2201.00879/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 3 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [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)
- [Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2006.01044/) (2020)
- [Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary](https://mlsanity.com/paper/2102.08308/) (2021)
- [Anomaly Detection via Controlled Sensing and Deep Active Inference](https://mlsanity.com/paper/2105.06288/) (2021)

## Built on by (most cited first, 2 of 2)

- [Anomaly Detection via Learning-Based Sequential Controlled Sensing](https://mlsanity.com/paper/2312.00088/) (2023)
- [Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases](https://mlsanity.com/paper/2403.10112/) (2024)

## Abstract (arXiv)

We address the problem of monitoring a set of binary stochastic processes and generating an alert when the number of anomalies among them exceeds a threshold. For this, the decision-maker selects and probes a subset of the processes to obtain noisy estimates of their states (normal or anomalous). Based on the received observations, the decisionmaker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations. When the decision is to continue, it then decides whether to collect observations at the next time instant or defer it to a later time. If it chooses to collect observations, it further determines the subset of processes to be probed. To devise this three-step sequential decision-making process, we use a Bayesian formulation wherein we learn the posterior probability on the states of the processes. Using the posterior probability, we construct a Markov decision process and solve it using deep actor-critic reinforcement learning. Via numerical experiments, we demonstrate the superior performance of our algorithm compared to the traditional model-based algorithms.