What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is integrated as an entire neural network model. The model learns to predict environmental state transitions by self-exploration and generating motor actions by sampling stochastic internal states z . Habitual behavior, which is obtained from the prior distribution of z , is acquired by reinforcement learning. Goal-directed behavior is determined from the posterior distribution of z by planning, using active inference , to minimize the }  free energy for goal observation . We demonstrate the effectiveness of the proposed framework by experiments in a sensorimotor navigation task with camera observations and continuous motor actions.
What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is integrated as an entire neural network model. The model learns to predict environmental state transitions by self-exploration and generating motor actions by sampling stochastic internal states z . Habitual behavior, which is obtained from the prior distribution of z , is acquired by reinforcement learning. Goal-directed behavior is determined from the posterior distribution of z by planning, using active inference which optimizes the past, current and future z} by minimizing the variational free energy for the desired future observation constrained by the observed sensory sequence . We demonstrate the effectiveness of the proposed framework by experiments in a sensorimotor navigation task with camera observations and continuous motor actions.
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