2023/02/17 by Tung Nguyen, Nguyen, Tung, Johane Takeuchi +1
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #I.2.0 #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2302.08748
openalex publication_date 2023/02/17 · openalex created_date 2023/02/21 · openalex updated_date 2026/07/28
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.