Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points
For analog circuit designers, this method improves sample efficiency and generalization in computationally expensive black-box optimization, but it is incremental as it builds on existing RL with a reset strategy.
Lighthouse RL introduces a strategic reset mechanism for reinforcement learning-based analog circuit sizing, achieving up to 1.72x faster optimization, 100% success rate (vs. 0-87% for baselines), and 75% extrapolation success (vs. 0-50%).
In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing. Traditional methods lack generalization across different performance targets, while standard RL approaches waste resources exploring unpromising regions. Our method addresses these inefficiencies through a strategic reset strategy that initializes episodes from high-performing configurations discovered during training, called "lighthouses". These states, which are closer to the target objectives, guide exploration toward promising regions. When compared to RL and Bayesian optimization methods from the literature, we demonstrate the effectiveness of our approach on a 2D benchmark problem and on two analog circuits, showing significant improvements in sample efficiency (up to 1.72x faster), optimization performance (100% vs. 0-87% success rate), generalization (75% vs. 0-50% extrapolation success), and objective maximization. This efficiency is particularly valuable for computationally expensive black-box optimization problems, and our reset strategy can be used as a plug-and-play enhancement for any RL-based optimization approach.