Before revision:  The optimization of congruent formulations of the free energy functional (variational and expected) , in active inference , enables agents to make inferences about the environment and select optimal behaviors. The agent achieves this by evaluating (sensory) evidence in relation to its internal generative model that entails beliefs about future (hidden) states and sequence of actions that it can choose. In contrast to analogous frameworks - by operating in a pure belief-based setting (free energy functional of beliefs about states) - active inference agents can carry out epistemic exploration and naturally account for uncertainty about their environment  .
After revision:  The optimization of congruent formulations of the free energy functional (variational and expected) , in active inference that are generally engineered in reinforcement learning; 2) to our knowledge, the first explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting (free energy functional of beliefs about states) - active inference agents can carry out epistemic exploration and naturally account for uncertainty about their environment .