This document describes how to use the function PWL2D_heuristic to build a piecewise linear function approximating a function within a given approximating error. It ...

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 ...

ABSTRACT: In this paper, the optimal control problem of parabolic integro-differential equations is solved by gradient recovery based two-grid finite element method. Piecewise linear functions are ...

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 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 ...

Abstract: The stochastic economic dispatch (SED) problem of active distribution network (ADN) is computationally intractable for traditional algorithms due to the randomness, nonlinearity, 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 ...