  Many challenging real-world problems require the deployment of ensembles
multiple complementary learning models to reach acceptable performance levels.
While effective, applying the entire ensemble to every sample is costly and
often unnecessary. Deep Reinforcement Learning (DRL) offers a cost-effective
alternative, where detectors are dynamically chosen based on the output of
their predecessors, with their usefulness weighted against their computational
cost. Despite their potential, DRL-based solutions are not widely used in this
capacity, partly due to the difficulties in configuring the reward function for
each new task, the unpredictable reactions of the DRL agent to changes in the
data, and the inability to use common performance metrics (e.g., TPR/FPR) to
guide the algorithm's performance. In this study we propose methods for
fine-tuning and calibrating DRL-based policies so that they can meet multiple
performance goals. Moreover, we present a method for transferring effective
security policies from one dataset to another. Finally, we demonstrate that our
approach is highly robust against adversarial attacks.
