2021/06/18 by Dongqi Han, Han, Dongqi, Kenji Doya +3
Computer Science · Neuroscience · #Artificial Intelligence (cs.AI) #Embodied and Extended Cognition #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural dynamics and brain function #Reinforcement Learning in Robotics #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2106.09938
openalex publication_date 2021/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.