lighthouse-rl-7fde1570·1 events·first seen Aliases: Lighthouse RL
Lighthouse RL is a reinforcement learning method for analog circuit sizing that initializes episodes from high-performing configurations ('lighthouses') discovered during training, steering exploration toward promising regions. The approach claims up to 1.72x faster convergence, 100% success rate versus 0-87% for baselines, and improved generalization over standard RL and Bayesian optimization methods. The reset strategy is designed as a plug-and-play enhancement compatible with any RL-based optimizer. The application domain is analog circuit sizing, a computationally expensive black-box optimization problem.