Deep Reinforcement Learning and its Neuroscientific Implications
It addresses the need for neuroscientists to explore deep RL's implications, potentially advancing understanding of brain mechanisms, but it is incremental as a review.
The paper reviews deep reinforcement learning as a framework for studying learning, representation, and decision-making in neuroscience, highlighting its potential to offer new research tools and hypotheses for brain and behavior studies.
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.