Abstract
Active inference is a probabilistic framework for modeling intelligent agent behaviours, which drives by the principle of minimizing free energy. In this paper, we integrate the imitation learning method with active inference to minimize the expected free energy under the supervision of an expert model. The proposed approach affords explainable decision-making as a combination of self-information and novelty-seeking or exploratory behavior in a hierarchical generative model. A lane-changing driving scenario is demonstrated to verify the efficiency of the proposed framework that outperforms conventional Reinforcement learning methods.
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