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Apprentissage actif sous contraite de budget en robotique et en neurosciences computationnelles : Localisation robotique et modélisation comportementale en environnement non stationnaire.

Abstract : Decision-making is a vast domain of scientific research, investigated by several different disciplines, such as in Neuroscience to understand the processes underlying decision-making in animals, in Robotics to propose efficient and rapid decision-making algorithms working in a variety of tasks. From the point of view of Neuroscience, this problem is usually solved with online processes through models of sequential decision-making based on the reinforcement learning framework. From the point of view of Robotics, the primary objective is to come up with efficient solutions that work in the real world. However, nowadays in Robotics, researches most often neglect what we can call the budget and which concerns the inherent material limitations of a robot such as the computation time, the limited number of possible actions, or the limited life duration of the robot's batteries. In this PhD work, we propose to introduce the notion of budget as an explicit constraint in Robotics learning processes applied to a localization task. To do so, we first test a model based on recent developments in statistical learning, which can treat data under budget constraints either by limiting the number of processed data or by fixing an explicit time limitation. Moreover, in order to progress towards a online version of this type of budgeted learning algorithms, we discuss possible inspirations from computational neuroscience. Within this framework, the alternation between information seeking for localization and decisions to move within the environment can be indirectly linked to the exploration-exploitation trade-off. We finally present our contribution to the modeling of this trade-off in animals performing a non-stationary task under different levels of uncertainty, and make the link with bandit methods.
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https://tel.archives-ouvertes.fr/tel-01674828
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Nassim Aklil Présentée. Apprentissage actif sous contraite de budget en robotique et en neurosciences computationnelles : Localisation robotique et modélisation comportementale en environnement non stationnaire.. Neurosciences. Université Pierre & Marie Curie - Paris 6, 2017. Français. ⟨tel-01674828⟩

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