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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Veröffentlicht in:	Proceedings of the IEEE Conference on Decision & Control S. 5244 - 5251
Hauptverfasser:	Bernstein, Andrey, Chen, Yue, Colombino, Marcello, Dall'Anese, Emiliano, Mehta, Prashant, Meyn, Sean
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2019
Schlagworte:	Approximation algorithms Convergence Government Monte Carlo methods Optimization Perturbation methods Stochastic processes
ISSN:	2576-2370
Online-Zugang:	Volltext
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