Integrating theoretical models within machine learning models holds considerable promise for constructing efficient and robust models. In biology, however, the integration can be challenging because the behavioral rules described by theoretical models are not necessarily invariant, in contrast to problems in physics. Here we propose a hybrid architecture that hierarchically integrates biological pursuit models into deep reinforcement learning. Our approach facilitates seamless agent mode switching and rule-based action selection, demonstrating efficient navigation in a predator-prey environment. Interestingly, our results parallel the hunting behavior observed in nature, offering novel insights into biology. As our framework can be integrated with existing hybrid or gray box models, it paves the way for further exploration in this exciting cross-section of machine learning and biology.