Real-world decision-making problems are often partially observable, and many can be formulated as a Partially Observable Markov Decision Process (POMDP). When we apply reinforcement learning (RL) algorithms to the POMDP, reasonable estimation of the hidden states can help solve the problems. Furthermore, explainable decision-making is preferable, considering their application to real-world tasks such as autonomous driving cars. We proposed an RL algorithm that estimates the hidden states by end-to-end training, and visualize the estimation as a state-transition graph. Experimental results demonstrated that the proposed algorithm can solve simple POMDP problems and that the visualization makes the agent's behavior interpretable to humans.
@article{arxiv.2304.09769,
title = {End-to-End Policy Gradient Method for POMDPs and Explainable Agents},
author = {Soichiro Nishimori and Sotetsu Koyamada and Shin Ishii},
journal= {arXiv preprint arXiv:2304.09769},
year = {2023}
}