Physics-Based Reinforcement Learning for Photochromic Light Control

Photochromic materials enable reversible color changes when exposed to light of specific wavelengths. In the REPIX project, a novel AI-assisted exposure system is developed to control spatially resolved photochromic state transitions on functional surfaces.

The project combines physics-based optical modeling with reinforcement learning to enable intelligent light control strategies for industrial applications.

The goal of this thesis is to contribute to the development of a simulation-driven AI control framework for photochromic light

patterning. In particular:

- Development and implementation of a reinforcement learning framework for exposure control

- Design of state and action spaces for light modulation

- Definition of reward functions for color accuracy and pattern fidelity

- Evaluation of sim-to-real transfer strategies

Interested? We are eager to hear form you @ PROFACTOR.at

Detailed Link:

www.profactor.at/job/master-thesis-physics-based-simulation-reinforcement-learning-for-photochromic-light-control-w-m-d/