ArticleNature communications2024
High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- 192-dimensional wavelength-/mode-/polarization-multiplexed photonic architecture for highly parallel optical computing.National science review · 2026Article
- Editorial for the Special Issue on Recent Advances in Lithography.Micromachines · 2026Article
- End-to-end all-optical in-sensor computing system using photonic integrated circuits.Science advances · 2026Article
- Spatiotemporal immunomodulation with programmable biomaterials to promote musculoskeletal tissue regeneration.Bioactive materials · 2026Review
- Asymmetric Diffusion Metasurface-Based Lensless Monitoring and Uniform Illumination Systems for Optical Lithography.Research (Washington, D.C.) · 2026Article
- Programmable space-frequency linear transformations in photonic interlacing architectures.Scientific reports · 2025Article
- In-situ training in programmable photonic frequency circuits.Nanophotonics (Berlin, Germany) · 2025Article
- Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches.Light, science & applications · 2025Review
- The Goldilocks principle of learning unitaries by interlacing fixed operators with programmable phase shifters on a photonic chip.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning systems. However, using traditional electrical computers to simulate agent-environment interactions in RL models consumes tremendous computing resources, posing a significant challenge to the efficiency of RL. Here, we propose a universal framework that utilizes a photonic integrated circuit (PIC) to simulate the interactions in RL for improving the algorithm efficiency. High parallelism and precision on-chip optical interaction calculations are implemented with the assistance of link calibration in the hybrid architecture PIC. By introducing similarity information into the reward function of the RL model, PIC-RL successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. Our results validate the effectiveness of simulating RL algorithm interactions on the PIC platform, highlighting its potential to boost computing power in large-scale and sophisticated RL tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.