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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Vydané v:	Artificial intelligence Ročník 241; s. 103 - 130
Hlavní autori:	Leonetti, Matteo, Iocchi, Luca, Stone, Peter
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.12.2016 Elsevier Science Ltd
Predmet:	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
On-line prístup:	Získať plný text
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