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We consider the problem of displaying advertisements on web pages in the "cost per click" model, which necessitates to learn the appeal of visitors for the different advertisements in order to maximize the revenue. In a realistic context, the advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately...
This paper introduces a new algorithm, namely the equi-correlation network (ECON), to perform supervised classification, and regression. ECON is a kernelized LARS-like algorithm, by which we mean that ECON uses an l1 regularization to produce sparse estimators, ECON efficiently rides the regularization path to obtain the estimator associated to any regularization constant values, and ECON represents...
Feature discovery aims at finding the best representation of data. This is a very important topic in machine learning, and in reinforcement learning in particular. Based on our recent work on feature discovery in the context of reinforcement learning to discover a good, if not the best, representation of states, we report here on the use of the same kind of approach in the context of approximate dynamic...
In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data in a more informative form that facilitates and improves subsequent steps. As a "good'' set of basis functions result in better solutions and defining such functions becomes a challenge with increasing problem complexity,...
We consider the problem of on-line value function estimation in reinforcement learning. We concentrate on the function approximator to use. To try to break the curse of dimensionality, we focus on non parametric function approximators. We propose to fit the use of kernels into the temporal difference algorithms by using regression via the LASSO. We introduce the equi-gradient descent algorithm (EGD)...
Our research relates to multi-agent and oriented object modeling and simulation of the complex systems. Our research interest itself more particularly with system where the spatial and temporal component make a great part of system to model (for example, ecosystems or systems of production). Within the framework of this article, we will be interested in the flexible production systems.The simulation...
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