Fabrício Enembreck

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This article proposes and compares different interaction models for reinforcement learning based on multi-agent system. The cooperation during the learning process is crucial to guarantee the convergence to a good policy. The exchange of rewards among the agents during the interaction is a complex task and if it is inadequate it may cause delays in learning(More)
This paper presents a novel method for the classification of images that combines information extracted from the images and contextual information. The main hypothesis is that contextual information related to an image can contribute in the image classification process. First, independent classifiers are designed to deal with images and text. From the(More)
In this paper we propose a novel strategy for converging dynamic policies generated by adaptive agents, which receive and accumulate rewards for their actions. The goal of the proposed strategy is to speed up the convergence of such agents to a good policy in dynamic environments. Since it is difficult to have the good value for a state due to the(More)
This paper presents a novel hybrid learning method and performance evaluation methodology for adaptive autonomous agents. Measuring the performance of a learning agent is not a trivial task and generally requires long simulations as well as knowledge about the domain. A generic evaluation methodology has been developed to precisely evaluate the performance(More)