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

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.