Automated Antenna Design via Domain Knowledge-Informed Reinforcement Learning and Imitation Learning

In this study, we propose a novel approach for automating antenna design through the utilization of domain knowledge-informed reinforcement learning (RL) and imitation learning (IL). The proposed method allows the RL agent to learn from its interactions with the environment and to take actions that maximize the reward signal, resulting in the optimal policy. Despite the attractive advantages, the learning process of RL can be challenging and time-consuming, as the agent must explore a vast range of states, actions, and policies. To enhance the efficiency of the design process, we incorporate two key components into the proposed method. First, we utilize domain knowledge, such as the scaling property of antenna, to initialize the antenna parameters. This not only provides interpretability to the design process but also reduces the solution space. Additionally, we employ IL to pretrain the decision-making network within the RL algorithm, which significantly mitigates the data inefficiency issue for RL and reduces the time required to explore feasible solutions. The proposed method has been evaluated using the design of a slot filtering antenna as a case study. Simulation results indicate that the proposed method can achieve the antenna design for different frequencies and bandwidth metrics with at least 50% fewer adjusting steps than the traditional RL method, such as deep deterministic policy gradient (DDPG). The combination of RL and IL also results in a considerable performance boost compared to both pure IL and pure RL for antenna designs.