  All biological and artificial agents must learn and make decisions given
limits on their ability to process information. As such, a general theory of
adaptive behavior should be able to account for the complex interactions
between an agent's learning history, decisions, and capacity constraints.
Recent work in computer science has begun to clarify the principles that shape
these dynamics by bridging ideas from reinforcement learning, Bayesian
decision-making, and rate-distortion theory. This body of work provides an
account of capacity-limited Bayesian reinforcement learning, a unifying
normative framework for modeling the effect of processing constraints on
learning and action selection. Here, we provide an accessible review of recent
algorithms and theoretical results in this setting, paying special attention to
how these ideas can be applied to studying questions in the cognitive and
behavioral sciences.
