Goal-Directed Planning by Reinforcement Learning and Active Inference

• URL: http://arxiv.org/abs/2106.09938v2
• Date: Tue, 22 Jun 2021 10:14:01 GMT
• Title: Goal-Directed Planning by Reinforcement Learning and Active Inference
• Authors: Dongqi Han, Kenji Doya and Jun Tani
• Abstract summary: We propose a novel computational framework of decision making with Bayesian inference. Goal-directed behavior is determined from the posterior distribution of $z$ by planning. We demonstrate the effectiveness of the proposed framework by experiments in a sensorimotor navigation task with camera observations and continuous motor actions.
• Score: 16.694117274961016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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