Available only for arXiv papers.
In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under the influence of molecular noise. Through two case studies, we demonstrate RL\'s capabilities in navigating biological systems\' inherent non-linearity and high dimensionality. Our findings reveal that RL effectively identifies the onset of Hopf bifurcations and preserves biological plausibility within the optimized networks. However, challenges were encountered in achieving persistent oscillations and matching traditional algorithms\' computational speed. Despite these limitations, the study highlights RL\'s significant potential as an instrumental tool in computational biology, offering a novel perspective for exploring and optimizing oscillatory dynamics within complex biological systems. Our research establishes RL as a promising strategy for manipulating and designing intricate behaviors in biological networks.