Utilization of domain knowledge to improve POMDP belief estimation
This addresses the challenge of data efficiency in POMDP policy learning for applications requiring decision-making under uncertainty.
The paper tackles the problem of decision making under uncertainty in POMDPs by integrating domain knowledge into belief estimation using Jeffrey's rule and normalization, resulting in reduced data requirements and improved policy performance.
The partially observable Markov decision process (POMDP) framework is a common approach for decision making under uncertainty. Recently, multiple studies have shown that by integrating relevant domain knowledge into POMDP belief estimation, we can improve the learned policy's performance. In this study, we propose a novel method for integrating the domain knowledge into probabilistic belief update in POMDP framework using Jeffrey's rule and normalization. We show that the domain knowledge can be utilized to reduce the data requirement and improve performance for POMDP policy learning with RL.