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The field of Reinforcement Learning continues to show promise in solving old problems in new and innovative ways. Thanks to the algorithms’ ability to learn without an explicit set of labeled training data, the action, environment, reward approach has lured many researchers into framing old problems in this manner. This work focuses on utilizing a multi-agent reinforcement learning approach to develop an optimization algorithm which can be used for training gradient descent-based machine learning models. The work focuses on the agents’ abilities to collaboratively navigate a multi-dimension error space and locate an optimal solution for the model in training, and compares and contrasts the collaborative Reinforcement Learning approach with popular optimizers used today such as the Root Mean Square Propagation (RMSProp) [1] algorithm and the Adaptive Moment Estimation (Adam) algorithm [2]. The work will also discuss how the agents’ performance varies as the model in training gains complexity. Furthermore, the work will examine rare conditions under which the agents failed to find an optimal solution. Finally, the work will discuss similarities between machine learning model training and Multi-Domain Operations decision making. Additionally, the work will discuss how lessons learned during the agent training can be applied to developing decision agents for Multi Domain Operations.
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