Prior preference learning from experts: Designing a reward with active inference

• Published in: Neurocomputing (Amsterdam) Vol. 492; pp. 508 - 515
• Main Authors: Shin, Jin Young, Kim, Cheolhyeong, Hwang, Hyung Ju
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2022
• Subjects: Active Inference; Free energy principle; Reinforcement learning; Reward design; Free energy principle; Active Inference; Reward design; Reinforcement learning
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2021.12.042

Active inference may be defined as Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. An agent will choose an action that leads to its prior preference for a future observation. In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them. We extend the concept of expected free energy (EFE), which is a core quantity in active inference, and claim that EFE can be treated as a negative value function. Motivated by the concept of prior preference and a theoretical connection, we propose a simple but novel method for learning a prior preference from experts. This illustrates that the problem with inverse RL can be approached with a new perspective of active inference. Experimental results of prior preference learning show the possibility of active inference with EFE-based rewards and its application to an inverse RL problem.