High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit.

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

SUBMITTER: Li XK 

PROVIDER: S-EPMC10844654 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature