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Algorithmic Learning Theory - 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings (Paperback, 2013 ed.)
Sanjay Jain, Remi Munos, Frank Stephan, Thomas Zeugmann
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R1,595
Discovery Miles 15 950
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the 24th International
Conference on Algorithmic Learning Theory, ALT 2013, held in
Singapore in October 2013, and co-located with the 16th
International Conference on Discovery Science, DS 2013. The 23
papers presented in this volume were carefully reviewed and
selected from 39 submissions. In addition the book contains 3 full
papers of invited talks. The papers are organized in topical
sections named: online learning, inductive inference and
grammatical inference, teaching and learning from queries, bandit
theory, statistical learning theory, Bayesian/stochastic learning,
and unsupervised/semi-supervised learning.
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Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30-July 3, 2008, Revised and Selected Papers (Paperback, 2008 ed.)
Sertan Girgin, Manuel Loth, Remi Munos, Philippe Preux, Daniil Ryabko
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R1,560
Discovery Miles 15 600
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Ships in 10 - 15 working days
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Inthesummerof2008,
reinforcementlearningresearchersfromaroundtheworld gathered in the
north of France for a week of talks and discussions on reinfor-
ment learning, on how it could be made more e?cient, applied to a
broader range of applications, and utilized at more abstract and
symbolic levels. As a participant in this 8th European Workshop on
Reinforcement Learning, I was struck by both the quality and
quantity of the presentations. There were four full days of short
talks, over 50 in all, far more than there have been at any p-
vious meeting on reinforcement learning in Europe, or indeed,
anywhere else in the world. There was an air of excitement as
substantial progress was reported in many areas including Computer
Go, robotics, and ?tted methods. Overall, the work reported seemed
to me to be an excellent, broad, and representative sample of
cutting-edge reinforcement learning research. Some of the best of
it is collected and published in this volume. The workshopandthe
paperscollectedhere provideevidence thatthe ?eldof reinforcement
learning remains vigorous and varied. It is appropriate to re?ect
on some of the reasons for this. One is that the ?eld remains
focused on a pr- lem - sequential decision making - without
prejudice as to solution methods. Another is the existence of a
common terminology and body of theory
From Bandits to Monte-Carlo Tree Search covers several aspects of
the ""optimism in the face of uncertainty"" principle for large
scale optimization problems under finite numerical budget. The
monograph's initial motivation came from the empirical success of
the so-called ""Monte-Carlo Tree Search"" method popularized in
Computer Go and further extended to many other games as well as
optimization and planning problems. It lays out the theoretical
foundations of the field by characterizing the complexity of the
optimization problems and designing efficient algorithms with
performance guarantees. The main direction followed in this
monograph consists in decomposing a complex decision making problem
(such as an optimization problem in a large search space) into a
sequence of elementary decisions, where each decision of the
sequence is solved using a stochastic ""multi-armed bandit""
(mathematical model for decision making in stochastic
environments). This defines a hierarchical search which possesses
the nice feature of starting the exploration by a quasi-uniform
sampling of the space and then focusing, at different scales, on
the most promising areas (using the optimistic principle) until
eventually performing a local search around the global optima of
the function. This monograph considers the problem of function
optimization in general search spaces (such as metric spaces,
structured spaces, trees, and graphs) as well as the problem of
planning in Markov decision processes. Its main contribution is a
class of hierarchical optimistic algorithms with different
algorithmic instantiations depending on whether the evaluations are
noisy or noiseless and whether some measure of the local
''smoothness'' of the function around the global maximum is known
or unknown.
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