Functional gradient descent for n-tuple regression

n-tuple neural networks have recently been applied to a wide range of learning domains. However, for the particular area of regression, existing systems have displayed two shortcomings: little flexibility in the objective function being optimized and an inability to handle nonstationarity in an online learning setting. A novel n-tuple system is proposed to address these issues. The new architecture leverages the idea of functional gradient descent, drawing inspiration from its use in kernel methods. Furthermore, its capabilities are showcased in reinforcement learning tasks, which involves both nonstationary online learning and task-specific objective functions.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

n-tuple neural networks have been widely used in various learning domains, but existing systems have limitations in terms of objective function flexibility and handling nonstationarity in online learning. A novel n-tuple system is proposed to address these issues, and its capabilities are showcased in reinforcement learning tasks.