Angular contact ball bearings (ACBBs) are widely used for their structural and
economic advantages. However, achieving optimal design to meet performance requirements
remains challenging with traditional optimal design methods, which rely on initial population
selection and struggle to adapt to changing conditions. To address these challenges, this study
employs deep reinforcement learning (DRL) for ACBB design. The proposed DRL framework is
further enhanced by integrating transfer learning (TL), which accelerates learning when transi
tioning to new conditions by leveraging knowledge from previously trained models. The frame
work was evaluated for its applicability, sensitivity to initial states, and adaptability to varying
loads. The DRL agent identified lightweight ACBBs satisfying life and contact stress require
ments, closely matching true solutions. Furthermore, TL enabled rapid adaptation to new load
conditions, highlighting the efficiency of the proposed approach. These findings suggest that
DRL and TL provide a flexible and reliable methodology for ACBB design in dynamic environ
ments.