Recent Advances in Reinforcement Learning [electronic resource] : 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised and Selected Papers / edited by Scott Sanner, Marcus Hutter.
Contributor(s): Sanner, Scott [editor.]
| Hutter, Marcus [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Artificial Intelligence: 7188Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Edition: 1st ed. 2012.Description: XIII, 345 p. 98 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642299469.Subject(s): Artificial intelligence | Computer science | Algorithms | Application software | Database management | Computer science -- Mathematics | Mathematical statistics | Artificial Intelligence | Theory of Computation | Algorithms | Computer and Information Systems Applications | Database Management | Probability and Statistics in Computer ScienceAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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Springer Nature eBookSummary: This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.
This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.
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