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Reinforcement learning for optimal control of arm movements


Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

Schaal,  S
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Theodorou, E., Peters, J., & Schaal, S. (2007). Reinforcement learning for optimal control of arm movements. Poster presented at 37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007), San Diego, CA, USA.

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Every day motor behavior consists of a plethora of challenging motor skills from discrete movements such as reaching and throwing to rhythmic movements such as walking, drumming and running. How this plethora of motor skills can be learned remains an open question. In particular, is there any unifying computational framework that could model the learning process of this variety of motor behaviors and at the same time be biologically plausible? In this work we aim to give an answer to these questions by providing a computational framework that unifies the learning mechanism of both rhythmic and discrete movements under optimization criteria, i.e., in a non-supervised trial and error fashion. Our suggested framework is based on Reinforcement Learning, which is mostly considered as too costly to be a plausible mechanism for learning complex limb movement. However, recent work on reinforcement learning with policy gradients combined with parameterized movement primitives allows novel and more efficient algorithms. By using the representational power of such motor primitives we show how rhythmic motor behaviors such as walking, squashing and drumming as well as discrete behaviors like reaching and grasping can be learned with biologically plausible algorithms. Using extensive simulations and by using different reward functions we provide results that support the hypothesis that Reinforcement Learning could be a viable candidate for motor learning of human motor behavior when other learning methods like supervised learning are not feasible.