Quasi-Stochastic Approximation and Off-Policy Reinforcement Learning

The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic frameworks for reinforcement learning (RL), and often an efficient approach to solving (or approximating the solution to) complex optimal control problems. However, in many cases practitioners are unable to appl...

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Bibliographic Details

Published in:	Proceedings of the IEEE Conference on Decision & Control pp. 5244 - 5251
Main Authors:	Bernstein, Andrey, Chen, Yue, Colombino, Marcello, Dall'Anese, Emiliano, Mehta, Prashant, Meyn, Sean
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2019
Subjects:	Approximation algorithms Convergence Government Monte Carlo methods Optimization Perturbation methods Stochastic processes
ISSN:	2576-2370
Online Access:	Get full text
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