Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points
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 Bayesi
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- PossiblePossibly related (embedding) · 52%Algorithm optimizes machine learning techniques that use linear, tunable resistor networks - aip.org →
- LinkedLinked via arxiv author · 85%Mustafa Emre Gürsoy →
“Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points”
- LinkedLinked via arxiv author · 85%Stefan Uhlich →
“Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points”
- LinkedLinked via arxiv author · 85%Ryoga Matsuo →
“Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points”
- LinkedLinked via arxiv author · 85%Yağız Gençer →
“Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points”
- LinkedLinked via arxiv author · 85%Arun Venkitaraman →
“Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points”
- LinkedLinked via arxiv author · 85%Chia-Yu Hsieh →
“Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points”
- LinkedLinked via arxiv author · 85%Andrea Bonetti →
“Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points”