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Conference Papers Year : 2020

Adaptive coordination of multiple learning strategies in brains and robots


Engineering approaches to machine learning (including robot learning) typically seek for the best learning algorithm for a particular problem, or a set problems. In contrast, the mammalian brain appears as a toolbox of different learning strategies, so that any newly encountered situation can be autonomously learned by an animal with a combination of existing learning strategies. For example, when facing a new navigation problem, a rat can either learn a map of the environment and then plan to find a path to its goal within this map. Alternatively, it can learn sequences of egocentric movements in response to identifiable features of the environment. For about 15 years, computational neuroscientists have searched for the mammalian brain's coordination mechanisms which enable it to find efficient, if not necessarily optimal, combinations of existing learning strategies to solve new problems. Understanding such coordination principles of multiple learning strategies could have great implications in robotics, to enable robots to autonomously determine which learning strategies are appropriate in different contexts. Here, we review some of the main neuroscience models for the coordination of learning strategies and present some of the early results obtained when applying these models to robot learning. We moreover highlight important energy costs which can be reduced with such bio-inspired solutions compared to current deep reinforcement learning approaches. We conclude by sketching a roadmap for further developing such bio-inspired hybrid learning approaches to robotics.
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hal-03277525 , version 1 (03-07-2021)



Mehdi Khamassi. Adaptive coordination of multiple learning strategies in brains and robots. 9th International Conference on the Theory and Practice of Natural Computing (TPNC 2020), Dec 2020, Taoyuan, Taiwan. ⟨10.1007/978-3-030-63000-3_1⟩. ⟨hal-03277525⟩
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