  We address the problem of sequentially selecting and observing processes from
a given set to find the anomalies among them. The decision-maker observes a
subset of the processes at any given time instant and obtains a noisy binary
indicator of whether or not the corresponding process is anomalous. In this
setting, we develop an anomaly detection algorithm that chooses the processes
to be observed at a given time instant, decides when to stop taking
observations, and declares the decision on anomalous processes. The objective
of the detection algorithm is to identify the anomalies with an accuracy
exceeding the desired value while minimizing the delay in decision making. We
devise a centralized algorithm where the processes are jointly selected by a
common agent as well as a decentralized algorithm where the decision of whether
to select a process is made independently for each process. Our algorithms rely
on a Markov decision process defined using the marginal probability of each
process being normal or anomalous, conditioned on the observations. We
implement the detection algorithms using the deep actor-critic reinforcement
learning framework. Unlike prior work on this topic that has exponential
complexity in the number of processes, our algorithms have computational and
memory requirements that are both polynomial in the number of processes. We
demonstrate the efficacy of these algorithms using numerical experiments by
comparing them with state-of-the-art methods.
