  In this paper, we address the problem of detecting anomalies among a given
set of binary processes via learning-based controlled sensing. Each process is
parameterized by a binary random variable indicating whether the process is
anomalous. To identify the anomalies, the decision-making agent is allowed to
observe a subset of the processes at each time instant. Also, probing each
process has an associated cost. Our objective is to design a sequential
selection policy that dynamically determines which processes to observe at each
time with the goal to minimize the delay in making the decision and the total
sensing cost. We cast this problem as a sequential hypothesis testing problem
within the framework of Markov decision processes. This formulation utilizes
both a Bayesian log-likelihood ratio-based reward and an entropy-based reward.
The problem is then solved using two approaches: 1) a deep reinforcement
learning-based approach where we design both deep Q-learning and policy
gradient actor-critic algorithms; and 2) a deep active inference-based
approach. Using numerical experiments, we demonstrate the efficacy of our
algorithms and show that our algorithms adapt to any unknown statistical
dependence pattern of the processes.
