Classification without gradients: multi-agent reinforcement learning approach to optimization (Conference Presentation)

Amir Morcos; Hong Man; Brian Maguire; Aaron West

doi:10.1117/12.2664025

12 June 2023 Classification without gradients: multi-agent reinforcement learning approach to optimization (Conference Presentation)

Amir Morcos, Hong Man, Brian Maguire, Aaron West

Author Affiliations +

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 1253814 (2023) https://doi.org/10.1117/12.2664025
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Reinforcement Learning continues to show promise in solving problems in new ways. Recent publications have demonstrated how utilizing a reinforcement learning approach can lead to a superior policy for optimization. While previous works have demonstrated the ability to train without gradients, most recent works has focused on the simpler regression problems. This work will show how a Multi-Agent Reinforcement Learning approach can be used to optimize models in training without the need for the gradient of the loss function, and how this approach can benefit defense applications.

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Amir Morcos, Hong Man, Brian Maguire, and Aaron West "Classification without gradients: multi-agent reinforcement learning approach to optimization (Conference Presentation)", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 1253814 (12 June 2023); https://doi.org/10.1117/12.2664025

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KEYWORDS

Machine learning

Defense and security

Neck

Optimization (mathematics)

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