Recent Advances in Reinforcement Learning [electronic resource] : 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised Selected Papers / edited by Scott Sanner, Marcus Hutter.
By: Sanner, Scott [editor.].
Contributor(s): Hutter, Marcus [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 7188Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2012Description: XIII, 345 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642299469.Subject(s): Computer science | Computer software | Database management | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Computation by Abstract Devices | Algorithm Analysis and Problem Complexity | Information Systems Applications (incl. Internet) | Database Management | Probability and Statistics in Computer ScienceDDC classification: 006.3 Online resources: Click here to access online
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Springer eBooksSummary: 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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