Deep Reinforcement Learning and Its Neuroscientific Implications

Neuron. 2020 Aug 19;107(4):603-616. doi: 10.1016/j.neuron.2020.06.014. Epub 2020 Jul 13.


The emergence of powerful artificial intelligence (AI) 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 that has so far received less attention from neuroscientists but that may have profound neuroscientific implications: deep reinforcement learning (RL). 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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Algorithms
  • Decision Making
  • Deep Learning*
  • Models, Neurological*
  • Models, Psychological*
  • Neural Networks, Computer*
  • Neurosciences
  • Reinforcement, Psychology*