Abstract: To approximate functions of a single variable by using linear interpolation is routine in empirical studies. Here, we consider approximating functions of several variables in a similar ...

Abstract: In this paper we propose a novel distributed gradient-based two-time-scale algorithm for multi-agent off-policy learning of linear approximation of the optimal action-value function ...

This article studies the accuracy of two versions of Kydland and Prescott's (1980, 1982) procedure for approximating optimal decision rules in problems in which the objective fails to be quadratic and ...

This article describes three approximation methods I used to solve the growth model (Model 1) studied by the National Bureau of Economic Research's nonlinear rational-expectations-modeling group ...

This paper presents two sets of considerations on the use of approximations to estimate freight trip generation (FTG) and freight generation (FG) rates, based on a single variable. Following recent ...

We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we ...

This repository contains the implementation of a linear regression model designed to approximate an unknown function based on available data. The goal is to find a polynomial function that best fits ...