Is there Value in Reinforcement Learning?

TL;DR

Is there Value in reinforcement learning? challenges the assumption that explicit value representations are necessary to model biological decision making. The authors show that policy-gradient methods are not truly value-free, because learning relies on value estimates, and that both VB and PG are instances of Generalized Policy Iteration, distinguished by how they represent and update Value. They argue that the choice of modeling assumptions (risk, observability, discounting) shapes the notion of Value, and that evaluating model complexity should consider computational as well as statistical aspects. The work advocates a shift toward algorithmic-level analysis of models in cognitive science, with implications for interpreting neural data and for designing models that balance statistical and computational demands.

Abstract

Action-values play a central role in popular Reinforcement Learing (RL) models of behavior. Yet, the idea that action-values are explicitly represented has been extensively debated. Critics had therefore repeatedly suggested that policy-gradient (PG) models should be favored over value-based (VB) ones, as a potential solution for this dilemma. Here we argue that this solution is unsatisfying. This is because PG methods are not, in fact, "Value-free" -- while they do not rely on an explicit representation of Value for acting (stimulus-response mapping), they do require it for learning. Hence, switching to PG models is, per se, insufficient for eliminating Value from models of behavior. More broadly, the requirement for a representation of Value stems from the underlying assumptions regarding the optimization objective posed by the standard RL framework, not from the particular algorithm chosen to solve it. Previous studies mostly took these standard RL assumptions for granted, as part of their conceptualization or problem modeling, while debating the different methods used to optimize it (i.e., PG or VB). We propose that, instead, the focus of the debate should shift to critically evaluating the underlying modeling assumptions. Such evaluation is particularly important from an experimental perspective. Indeed, the very notion of Value must be reconsidered when standard assumptions (e.g., risk neutrality, full-observability, Markovian environment, exponential discounting) are relaxed, as is likely in natural settings. Finally, we use the Value debate as a case study to argue in favor of a more nuanced, algorithmic rather than statistical, view of what constitutes "a model" in cognitive sciences. Our analysis suggests that besides "parametric" statistical complexity, additional aspects such as computational complexity must also be taken into account when evaluating model complexity.