A synthesis of automated planning and reinforcement learning for efficient, robust decision-making

Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain,...

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Bibliographic Details
Published in:	Artificial intelligence Vol. 241; pp. 103 - 130
Main Authors:	Leonetti, Matteo, Iocchi, Luca, Stone, Peter
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.12.2016 Elsevier Science Ltd
Subjects:	Answer set programming Approximation Automated planning Automation Autonomous robot Decision making Decisions Expert systems Learning Machine learning Model accuracy Reinforcement Reinforcement learning Reliability Robot learning Robots Tasks Autonomous robot Robot learning Automated planning Answer set programming Reinforcement learning
ISSN:	0004-3702, 1872-7921
Online Access:	Get full text
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