Abstract
Balancing exploration and exploitation is one of the central problems in reinforcement learning. We review recent studies that have identified multiple algorithmic strategies underlying exploration. In particular, humans use a combination of random and uncertainty-directed exploration strategies, which rely on different brain systems, have different developmental trajectories, and are sensitive to different task manipulations. Humans are also able to exploit sophisticated structural knowledge to aid their exploration, such as information about correlations between options. New computational models, drawing inspiration from machine learning, have begun to formalize these ideas and offer new ways to understand the neural basis of reinforcement learning.
