"Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is …
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is g…
The four-volume set LNCS 6016 - 6019 constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2010, held in Fukuoka, Japan, in March 2010. The four volumes contain papers presenting a wealth of original research re…
A timely introduction to the latest modeling and simulation techniques Object-oriented modeling is a fast-growing area of modeling and simulation that provides a structured, computer-supported way of doing mathematical and equation-based modeling. Modelica is today's most promis…
A timely introduction to the latest modeling and simulation techniques Object-oriented modeling is a fast-growing area of modeling and simulation that provides a structured, computer-supported way of doing mathematical and equation-based modeling. Modelica is today's most promis…
This book constitutes the thoroughly refereed post-conference proceedings of 19th International Workshop on Power and Timing Modeling, Optimization and Simulation, PATMOS 2009, featuring Integrated Circuit and System Design, held in Delft, The Netherlands during September 9-11, …
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Why are the many highly capable autonomous robots that have been promised for novel applications driven by society, industry, and research not available - day despite the tremendous progress in robotics science and systems achieved during the last decades? Unfortunately, steady …
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