Escape routing using deep reinforcement learning

Escape routing is a critical task in the design of printed circuit boards (PCB) and integrated circuits (IC), aiming to establish effective connections among multiple pin points while avoiding path overlaps and interference from obstacles. This paper proposes a Deep Reinforcement Learning (DRL)-based solution utilizing a Deep Q-Network (DQN) to address the escape routing problem. The problem is modeled as a Markov Decision Process (MDP), and the agent learns effective routing strategies through interactions with the environment. Experiments were conducted in a 25×26 simulated grid environment, testing scenarios with 30, 60, and 90 pin points. This study highlights the potential of Deep Reinforcement Learning in solving escape routing problems, offering a novel approach to addressing routing challenges in PCB design.