Linear reinforcement learning in planning, grid fields, and cognitive control

Abstract

Models of decision making have so far been unable to account for how humans’ choices can be flexible yet efficient. Here the authors present a linear reinforcement learning model which explains both flexibility, and rare limitations such as habits, as arising from efficient approximate computation