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

• URL: http://arxiv.org/abs/2304.09769v1
• Date: Wed, 19 Apr 2023 15:45:52 GMT
• Title: End-to-End Policy Gradient Method for POMDPs and Explainable Agents
• Authors: Soichiro Nishimori, Sotetsu Koyamada and Shin Ishii
• Abstract summary: We propose 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.
• Score: 2.1700203922407493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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