This tutorial provides practical guidance for researchers seeking to extend DP and RL techniques to larger domains through linear value function approximation. The practical algorithms and empirical ...

Formulate linear and integer programming problems for solving commonly encountered optimization problems. Understand how approximation algorithms compute solutions that are guaranteed to be within ...

It will focus on how these methods have been combined with parametric function approximation, including deep learning, to find good approximate solutions to problems that are otherwise too large to be ...

Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning I think I know this paper reasonably well. I already wrote a lot about it; click here for my details (blog ...

This paper demonstrates a Remez exchange algorithm applicable to approximation of real-valued continuous functions of a real variable by polynomials of degree smaller than n with various linear ...

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 presents a method for estimating a linear time-varying approximation of a general class of nonlinear time-varying (NLTV) systems. It starts from noisy measurements of the response of the ...