| Title |
Sample-Efficient Reinforcement Learning for Analog Circuit Optimization with Intrinsic Reward |
| Authors |
(SuMin Oh) ; (HyunJin Kim) |
| DOI |
https://doi.org/10.5573/JSTS.2025.25.5.469 |
| Keywords |
Analog circuit optimization; conditional variational autoencoder; intrinsic reward |
| Abstract |
Analog circuit optimization remains a challenge due to its high-dimensional design space and the prohibitive cost of simulations. To improve sample efficiency, we propose a reinforcement learning (RL) framework that uses intrinsic rewards, enabling agents to efficiently explore novel circuit designs. Furthermore, by leveraging a conditional variational autoencoder (CVAE) for reconstruction-based intrinsic reward, our approach enhances exploration and accelerates convergence in circuit optimization. Experimental results on practical circuits demonstrate significant performance improvements over counterparts without using a reconstruction-based intrinsic reward. |