Titlebook: Recent Advances in Reinforcement Learning; 9th European Worksho Scott Sanner,Marcus Hutter Conference proceedings 2012 Springer-Verlag Berl

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?1-Penalized Projected Bellman Residualomes at the cost of a higher computational complexity if only a part of the regularization path is computed. Nevertheless, our approach ends up to a supervised learning problem, which let envision easy extensions to other penalties.

Proponent

Conference proceedings 2012e 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 learn

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cushion

Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement Learningore knowledgeable than they are today. Natural environments are composed of objects, and the possibilities to manipulate them are highly structured due to the general laws governing our relational world. All these need to be acknowledged when we want to realize thinking robots that efficiently learn

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Invited Talk: PRISM – Practical RL: Representation, Interaction, Synthesis, and Mortalityoven to converge in small finite domains, and then just hope for the best. This talk will advocate instead designing algorithms that adhere to the constraints, and indeed take advantage of the opportunities, that might come with the problem at hand. Drawing on several different research threads with

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troponins

Automatic Discovery of Ranking Formulas for Playing with Multi-armed Banditsining a grammar made of basic elements such as for example addition, subtraction, the max operator, the average values of rewards collected by an arm, their standard deviation etc., and by exploiting this grammar to generate and test a large number of formulas. The systematic search for good candida

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Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control and the other model free. These algorithms come with the possibility of having non-squared loss functions which is novel in reinforcement learning, and seems to come with empirical advantages. We further extend a previous gradient based algorithm to the case of full control, by using generalized po

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