Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization

TL;DR

This paper presents a model-free reinforcement learning method using Proximal Policy Optimization for in situ training of diffractive optical processors, improving convergence speed and stability without prior system modeling.

Contribution

It introduces a novel PPO-based in situ training approach for optical processors, addressing limitations of existing methods and handling real-world imperfections effectively.

Findings

Demonstrated improved convergence in optical tasks

Achieved stable training despite hardware imperfections

Validated across multiple optical applications

Abstract

Optical computing holds promise for high-speed, energy-efficient information processing, with diffractive optical networks emerging as a flexible platform for implementing task-specific transformations. A challenge, however, is the effective optimization and alignment of the diffractive layers, which is hindered by the difficulty of accurately modeling physical systems with their inherent hardware imperfections, noise, and misalignments. While existing in situ optimization methods offer the advantage of direct training on the physical system without explicit system modeling, they are often limited by slow convergence and unstable performance due to inefficient use of limited measurement data. Here, we introduce a model-free reinforcement learning approach utilizing Proximal Policy Optimization (PPO) for the in situ training of diffractive optical processors. PPO efficiently reuses in situ measurement data and constrains policy updates to ensure more stable and faster convergence. We experimentally validated our method across a range of in situ learning tasks, including targeted energy focusing through a random diffuser, holographic image generation, aberration correction, and optical image classification, demonstrating in each task better convergence and performance. Our strategy operates directly on the physical system and naturally accounts for unknown real-world imperfections, eliminating the need for prior system knowledge or modeling. By enabling faster and more accurate training under realistic experimental constraints, this in situ reinforcement learning approach could offer a scalable framework for various optical and physical systems governed by complex, feedback-driven dynamics.