Deep Reinforcement Learning and its Neuroscientific Implications

• URL: http://arxiv.org/abs/2007.03750v1
• Date: Tue, 7 Jul 2020 19:27:54 GMT
• Title: Deep Reinforcement Learning and its Neuroscientific Implications
• Authors: Matthew Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson
• Abstract summary: The emergence of powerful artificial intelligence is defining new research directions in neuroscience. Deep reinforcement learning (Deep RL) offers a framework for studying the interplay among learning, representation and decision-making. Deep RL offers a new set of research tools and a wide range of novel hypotheses.
• Score: 19.478332877763417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. However, there is another area of recent AI work which has so far received less attention from neuroscientists, but which may have profound neuroscientific implications: deep reinforcement learning. Deep RL offers a comprehensive framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. In the present review, we provide a high-level introduction to deep RL, discuss some of its initial applications to neuroscience, and survey its wider implications for research on brain and behavior, concluding with a list of opportunities for next-stage research.

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