Effective decision making involves flexibly relating past experiences and
relevant contextual information to a novel situation. In deep reinforcement
learning (RL), the dominant paradigm is for an agent to amortise information
that helps decision making into its network weights via gradient descent on
training losses. Here, we pursue an alternative approach