mlsanityData to 14 Oct 2026

Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

arXiv 2607.14008 ↗First posted 15 Jul 2026cs.LG cs.ARcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 31 references that are in the map. First posted on arXiv 15 Jul 2026.

Builds on

11 of 31 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Graph Attention Networkscited by 8,923
  3. 2016Layer Normalizationcited by 5,669
  4. 2015Continuous control with deep reinforcement learningcited by 3,407
  5. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  6. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  7. 2017Hindsight Experience Replaycited by 1,136
  8. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
  9. 2019BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networkscited by 11
  10. 2024AnalogGym: An Open and Practical Testing Suite for Analog Circuit Synthesiscited by 9
  11. 2022RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RLcited by 5

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  2. 2024M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling
  3. 2025Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits
  4. 2025GLOVA: Global and Local Variation-Aware Analog Circuit Design with Risk-Sensitive Reinforcement Learning
  5. 2025AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
  6. 2026AstRL: Analog and Mixed-Signal Circuit Synthesis with Deep Reinforcement Learning
  7. 2026ARCS: Autoregressive Circuit Synthesis with Topology-Aware Graph Attention and Spec Conditioning
  8. 2025Enhancing Reinforcement Learning for the Floorplanning of Analog ICs with Beam Search