Deep Reinforcement Learning and its Neuroscientific Implications

TL;DR

Deep reinforcement learning provides a new framework for understanding brain functions related to learning, decision-making, and representation, with significant implications for neuroscience research.

Contribution

This paper introduces deep reinforcement learning to neuroscientists, highlighting its potential to generate novel hypotheses and research tools for understanding brain mechanisms.

Findings

Deep RL offers insights into neural decision-making processes.

Initial applications demonstrate its relevance to neuroscience.

Survey of future research opportunities in brain and behavior.

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.