When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design
Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled parameters requires costly full-wave simulations. Deep Q-network (DQN) optimization can reuse simulated transitions to guide edits, yet which value-learning mechanisms remain reliable under tight simulation budgets is unknown. We address this gap by comparing baseline DQN and six value-based variants for a seven-variable PCSEL design under a shared objective, simulator, 83-call budget, and four matched initializations. Beyond endpoints, we analyze sample efficiency, policy behavior, and physical response to separate learning gains from favorabl...
| Authors | Longying Wen, Feiyang Wu, Jinglin Yu et al. |
|---|---|
| Primary category | physics.optics |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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