Uma arquitetura híbrida aplicada em problemas de aprendizagem por reforço

TL;DRAbstract

With the evergrowing use of cognitive systems in various applications, it has been created a high expectation and a large demand for machines more and more autonomous, intelligent and creative in real world problem solving. In several cases, the challenges ask for high adaptive and learning capability. This work deals with the concepts of reinforcement learning, and reasons on the main solution approaches and problem variations. Subsequently, it builds a hybrid proposal incorporating other machine learning ideas, so that the proposal is validated with simulated experiments. The experiments allow to point out the main advantages of the proposed methodology, founded on its capability to handle continuous space environments, and also to learn an optimal policy while following an exploratory policy. The proposed architecture is hybrid in the sense that it is based on a multi-layer perceptron neural network coupled with a function approximator called wire-fitting. The referred architecture

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