# Deep Actor-Critic Reinforcement Learning for Anomaly Detection

arXiv: https://arxiv.org/abs/1908.10755 · HTML: https://mlsanity.com/paper/1908.10755/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [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)
- [Wireless Sensor Networks anomaly detection using Machine Learning: A Survey](https://mlsanity.com/paper/2303.08823/) (2023)
- [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)

Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks. In this paper, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. We provide simulation results for both the training phase and testing phase, and compare the proposed framework with the Chernoff test in terms of claim delay and loss.