Policy gradient in Lipschitz Markov Decision Processes

This paper is about the exploitation of Lipschitz continuity properties for Markov Decision Processes to safely speed up policy-gradient algorithms. Starting from assumptions about the Lipschitz continuity of the state-transition model, the reward function, and the policies considered in the learnin...

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Bibliographic Details
Published in:	Machine learning Vol. 100; no. 2-3; pp. 255 - 283
Main Authors:	Pirotta, Matteo, Restelli, Marcello, Bascetta, Luca
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.09.2015 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computer Science Continuity Control Control systems Decision making models Learning Machine learning Markov analysis Markov processes Mathematical models Mechatronics Natural Language Processing (NLP) Policies Robotics Simulation and Modeling Policy gradient algorithm Markov Decision Process Reinforcement learning Lipschitz continuity
ISSN:	0885-6125, 1573-0565
Online Access:	Get full text
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