  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.
