Prior Preference Learning from Experts:Designing a Reward with Active Inference
This work provides a novel perspective on inverse reinforcement learning by integrating active inference, potentially benefiting researchers in AI and robotics, though it appears incremental as it builds on existing theories.
The paper tackles the problem of learning prior preferences from experts by connecting active inference with reinforcement learning, showing that expected free energy can be treated as a negative value function and applying this to inverse reinforcement learning with experimental validation.
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.