Framework for design optimization using deep reinforcement learning

We propose a framework for design optimization using deep reinforcement learning and study its capabilities. Reinforcement learning is highly generalizable to unseen system configurations for similar optimization problems. In industrial fields, product requirements vary depending on specifications and the requirements are often similar but slightly different from each other. We utilize reinforcement learning to optimize products to meet those slightly different requirements. In the proposed framework, an agent is trained in advance and used to find the optimal solution given a set of requirements. We apply the proposed framework to optimize the airfoil angle of attack and demonstrate its generalization capabilities.