Reinforcement Learning

Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 9780262193986
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The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

Introduction To Machine Learning

Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262028182
Size: 36.32 MB
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Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer ...

Adaptive Representations For Reinforcement Learning

Author: Simon Whiteson
Publisher: Springer Science & Business Media
ISBN: 3642139310
Size: 48.57 MB
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This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task.

Machine Learning And Knowledge Discovery In Databases

Author: José L. Balcázar
Publisher: Springer Science & Business Media
ISBN: 3642159389
Size: 67.94 MB
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This efficiency is gained at the cost of additional computation time for the
simulations, which generate sample trajectories of candidate policies during
optimization of the expected improvement. Overall, MBOA ... Brochu, E., Cora, V.,
de Freitas, N.: A tutorial on bayesian optimization of expensive cost functions,
with application to active user modeling and hierarchical reinforcement learning.
Technical Report ... Sutton, R., Barto, A.G.: Reinforcement Learning: An
Introduction. MIT Press ...

Efficient Reinforcement Learning Using Gaussian Processes

Author: Marc Peter Deisenroth
Publisher: KIT Scientific Publishing
ISBN: 3866445695
Size: 24.62 MB
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Integrated Architectures for Learning, Planning, and Reacting Based on
Approximate Dynamic Programming. In Proceedings of the Seventh International
Conference on Machine Learning, pages 215–224. Morgan Kaufman Publishers.
Cited on pp. 31 and 116. Sutton, R. S. and Barto, A. G. (1998). Reinforcement
Learning: An Introduction. Adaptive Computation and Machine Learning. The MIT
Press, Cambridge, MA, USA. Cited on pp. 34, 41, and 113. Szepesv ́ari, C. (2010)
.

Recent Advances In Reinforcement Learning

Author: Sertan Girgin
Publisher: Springer
ISBN: 3540897224
Size: 53.89 MB
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... of the filter, and adaptive observation noise could be nec- essary for some
more complex tasks, as it is formally input-dependent. Other parametrization for
the Q-function can also be considered. Moreover, there are a few technical
issues, as the search of maxima over actions. Last but not least theoretical
convergence may be a problem and should be studied. References 1. Sutton,
R.S., Barto, A.G.: Reinforcement Learning: An Introduction (Adaptive
Computation and Machine ...

Proceedings 21 Workshop Computational Intelligence Dortmund 1 2 Dezember 2011

Author: Frank Hoffmann
Publisher: KIT Scientific Publishing
ISBN: 3866447434
Size: 56.27 MB
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[6] Sutton, R. S.; Barto, A. G.: Reinforcement Learning: An Introduction (Adaptive
Computation and Machine Learning). MIT Press. URL http://www.cse.iitm.ac.in/~
cs670/ book/the-book.html. 1998. [7] Granmo, O.-C.: A Bayesian Learning
Automaton for Solving Two-Armed Bernoulli Bandit Problems. In: Proceedings of
the 2008 Seventh International Conference on Machine Learning and
Applications, S. 23–30. Washington, DC, USA: IEEE Computer Society. ISBN 978
-0-7695-3495-4.

Machine Learning And Knowledge Discovery In Databases

Author: Paolo Frasconi
Publisher: Springer
ISBN: 331946227X
Size: 22.29 MB
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ACM (2005) Stolle, M., Precup, D.: Learning options in reinforcement learning. In:
Koenig, S., Holte, R.C. (eds.) SARA 2002. LNCS, vol. 2371, pp. 212–223.
Springer, Heidelberg (2002) Sutton, R.S.: Learning to predict by the methods of
temporal differences. Mach. Learn. 3(1), 9–44 (1988) Sutton, R.S., Barto, A.G.:
Reinforcement Learning: An Introduction. Adaptive Computation and Machine
Learning. MIT Press, Cambridge (1998) Sutton, R.S., Precup, D., Singh, S.:
Between MDPs ...

Recent Advances Of Neural Network Models And Applications

Author: Simone Bassis
Publisher: Springer Science & Business Media
ISBN: 3319041290
Size: 29.29 MB
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11. 12. 13. 14. 15. 16. 17. 18. Barto, A.G., Sutton, R.S.: Reinforcement Learning:
An Introduction. Adaptive Computation and Machine Learning. The MIT Press (
1998) 2. Bekiros, S.D.: Heterogeneous trading strategies with adaptive fuzzy
Actor-Critic reinforcement learning: A behavioral approach. Journal of Economic
Dynamics & Control 34, 1153– 1170 (2010) 3. Bertoluzzo, F., Corazza, M.:
Making financial trading by recurrent reinforcement learning. In: Apolloni, B.,
Howlett, R.J., Jain, ...