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What is the difference between goal-directed and habitual behavior? Wepropose a novel computational framework of decision making with Bayesianinference, in which everything is integrated as an entire neural network model.The model learns to predict environmental state transitions by self-explorationand generating motor actions by sampling stochastic internal states ${z}$.Habitual behavior, which is obtained from the prior distribution of ${z}$, isacquired by reinforcement learning. Goal-directed behavior is determined fromthe posterior distribution of ${z}$ by planning, using active inference whichoptimizes the past, current and future ${z}$ by minimizing the variational freeenergy for the desired future observation constrained by the observed sensorysequence. We demonstrate the effectiveness of the proposed framework byexperiments in a sensorimotor navigation task with camera observations andcontinuous motor actions.

Goal-Directed Planning by Reinforcement Learning and Active Inference