Psychological and Neural Evidence for Reinforcement Learning: A Survey

Reinforcement learning methods have been recently been very successful in complex sequential tasks like playing Atari games, Go and Poker. Through minimal input from humans, these algorithms can learn to perform complex tasks from scratch, just through rewards obtained through interaction with their environment. While there certainly has been considerable independent innovation in the area to produce such results, many core ideas in RL are inspired by animal learning, psychology and neuroscience phenomena. Moreover, these algorithms are now helping advance neuroscience and cognitive science research by serving as a computational model for many characteristic features of brain functioning. In this paper, we review a number of key findings in neuroscience and human behavior that provide evidence for the involvement of reinforcement learning in human learning. Finally, we discuss how these findings have provided inspiration for new RL algorithms.