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arXiv · 2304.09769

End-to-End Policy Gradient Method for POMDPs and Explainable Agents

Soichiro Nishimori·Sotetsu Koyamada·Shin Ishii
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Abstract

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.

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Soichiro Nishimori, Sotetsu Koyamada, Shin Ishii. 2023-04-19. End-to-End Policy Gradient Method for POMDPs and Explainable Agents. https://arxiv.org/abs/2304.09769

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