Before revision:  We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration - and account for uncertainty about their environment - in a Bayes optimal fashion.
After revision:  We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration - and account for uncertainty about their environment in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in RL is removed in active inference, where reward can simply be treated as another observation; even in the total absence of rewards, agent behaviors are learned through preference learning.