###### Abstract  
Microstrip filters are widely used in high-frequency circuit design for signal frequency selection. However, designing these filters often requires extensive trial and error to achieve the desired performance metrics, leading to significant time costs. In this work, we propose an automated design flow for hairpin filters, a specific type of microstrip filter. We employ artificial neural network (ANN) modeling techniques to predict the circuit performance of hairpin filters, and leverage the efficiency of low-cost models to deploy reinforcement learning agents. Specifically, we use the proximal policy optimization (PPO) reinforcement learning algorithm to learn abstract design actions for the filters, allowing us to achieve automated optimization design. Through simulation results, we demonstrate the effectiveness of the proposed approach. By optimizing the geometric dimensions, we significantly improve the performance metrics of hairpin filters, and the trained agent successfully meets our specified design goals within 5 to 15 design steps. This work serves as a conceptual validation attempt to apply reinforcement learning techniques and pre-trained ANN models to automate MMIC filter design. It exhibits clear advantages in terms of time-saving and performance efficiency when compared to other optimization algorithms. h airpin filter; reinforcement learning; proximal policy optimization Article Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]. To ensure an effective design, microstrip filter designers typically need to go through several crucial steps [4; 5; 6]: 1. Determine the design specifications of the circuit and represent them using transfer functions; 2. Synthesize the transfer functions using an ideal lumped element network; 3. Due to the comparable wavelength of circuit elements in the microwave frequency range, it is necessary to transform the lumped circuit implementation into a distributed circuit implementation and confirm the equivalence between the lumped synthesis network and the desired distributed circuit structure; 4. In the optimization stage, adjust the dimensions of the actual filter structure to achieve the desired frequency response. The final step often requires engineers to iterate and modify the physical dimensions of the filter to meet the specified design requirements. This iterative process heavily relies on the engineer's expertise and design experience [7], which can be tedious and time-consuming. It also limits engineers from engaging in more innovative circuit structure development. Therefore, research on automating this design process continues to grow. Additionally, this process is constrained by the computational resources and time costs associated with electromagnetic simulation algorithms [8]. Depending on the complexity of the electromagnetic model, the computation time can range from minutes to hours [9].