Bounded rationality, that is, decision-making and planning under resource
limitations, is widely regarded as an important open problem in artificial
intelligence, reinforcement learning, computational neuroscience and economics.
This paper offers a consolidated presentation of a theory of bounded
rationality based on information-theoretic ideas. We provide a conceptual
justification for using the free energy functional as the objective function
for characterizing bounded-rational decisions. This functional possesses three
crucial properties: it controls the size of the solution space; it has Monte
Carlo planners that are exact, yet bypass the need for exhaustive search; and
it captures model uncertainty arising from lack of evidence or from interacting
with other agents having unknown intentions. We discuss the single-step
decision-making case, and show how to extend it to sequential decisions using
equivalence transformations. This extension yields a very general class of
decision problems that encompass classical decision rules (e.g. EXPECTIMAX and
MINIMAX) as limit cases, as well as trust- and risk-sensitive planning.

本文提出了基于信息理论思想的有限理性理论，并提供了将自由能量函数作为表征有限理性决策的目标函数的概念上的理据。该文讨论了单步决策以及如何使用等价变换将其扩展到序贯决策，扩展后得到的类别决策问题非常广泛，包括古典决策规则等极限情况以及可信和风险敏感规划。