Is there a canonical way to think of agency beyond reward maximisation? In
this paper, we show that any type of behaviour complying with physically sound
assumptions about how macroscopic biological agents interact with the world
canonically integrates exploration and exploitation in the sense of minimising
risk and ambiguity about states of the world. This description, known as active
inference, refines the free energy principle, a popular descriptive framework
for action and perception originating in neuroscience. Active inference
provides a normative Bayesian framework to simulate and model agency that is
widely used in behavioural neuroscience, reinforcement learning (RL) and
robotics. The usefulness of active inference for RL is three-fold. \emph{a})
Active inference provides a principled solution to the exploration-exploitation
dilemma that usefully simulates biological agency. \emph{b}) It provides an
explainable recipe to simulate behaviour, whence behaviour follows as an
explainable mixture of exploration and exploitation under a generative world
model, and all differences in behaviour are explicit in differences in world
model. \emph{c}) This framework is universal in the sense that it is
theoretically possible to rewrite any RL algorithm conforming to the
descriptive assumptions of active inference as an active inference algorithm.
Thus, active inference can be used as a tool to uncover and compare the
commitments and assumptions of more specific models of agency.

活动推理是一种规范性贝叶斯框架，能够模拟和建模智能代理行为，同时提供了对探索和利用之间的困境的原则性解决方案。