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.
After revision:  This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference , enables agents to make inferences about the environment and select optimal behaviors.