Topic: hippocampal memory

In reinforcement learning (RL), we try to learn a function f that maps a state representation s to actions a: a=f(s). Learning f is hard, and is typically performed in each environment. What if, instead, we learn how to rapidly construct s out of general building blocks, for which the same f always works? We demonstrate (1) that this is possible in spatial worlds using object- and goal-vector representations that exist in the brain and (2) that this ‘behavioural construction’ dramatically outperforms traditional RL in simple spatial problems. In a companion paper, we demonstrate that the “buil