High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit

Xuan-Kun Li, Jian-Xu Ma, Xiang-Yu Li, Jun-Jie Hu, Chuan-Yang Ding, Feng-Kai Han, Xiao-Min Guo, Xi Tan and Xian-Min Jin ()
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Xuan-Kun Li: Shanghai Jiao Tong University
Jian-Xu Ma: TuringQ Co., Ltd.
Xiang-Yu Li: TuringQ Co., Ltd.
Jun-Jie Hu: Shanghai Jiao Tong University
Chuan-Yang Ding: Shanghai Jiao Tong University
Feng-Kai Han: Shanghai Jiao Tong University
Xiao-Min Guo: TuringQ Co., Ltd.
Xi Tan: Shanghai Jiao Tong University
Xian-Min Jin: Shanghai Jiao Tong University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-45305-z

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