Evaluation of action spaces for reinforcement learning in optical design

Cailing Fu; Dominik Onyszkiewicz; Marco Kemmerling; Jochen Stollenwerk; Carlo Holly

doi:10.1117/12.3016630

18 June 2024 Evaluation of action spaces for reinforcement learning in optical design

Cailing Fu, Dominik Onyszkiewicz, Marco Kemmerling, Jochen Stollenwerk, Carlo Holly

Proceedings Volume 13017, Machine Learning in Photonics; 130170X (2024) https://doi.org/10.1117/12.3016630
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

Nowadays, sophisticated ray tracing software packages are used for the design of optical systems, including local and global optimization algorithms. Nevertheless, the design process is still time-consuming with many manual steps, taking days or even weeks until an optical design is finished. To address this shortcoming, with reinforcement learning, an agent can be trained to use ray tracing and optimization software designing an optical system. In this setting, the agent can modify the current state of the system with a predefined set of actions. One of the primary challenges is the selection of an appropriate action space. Different types of discrete and continuous action spaces are compared and their advantages and disadvantages in terms of the cumulated reward, convergence rate and resulting optical design are examined.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cailing Fu, Dominik Onyszkiewicz, Marco Kemmerling, Jochen Stollenwerk, and Carlo Holly "Evaluation of action spaces for reinforcement learning in optical design", Proc. SPIE 13017, Machine Learning in Photonics, 130170X (18 June 2024); https://doi.org/10.1117/12.3016630

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