Towards Using Fully Observable Policies for POMDPs

Autor:	Sulyok, András Attila, Karacs, Kristóf
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Partially Observable Markov Decision Process (POMDP) is a framework applicable to many real world problems. In this work, we propose an approach to solve POMDPs with multimodal belief by relying on a policy that solves the fully observable version. By defininig a new, mixture value function based on the value function from the fully observable variant, we can use the corresponding greedy policy to solve the POMDP itself. We develop the mathematical framework necessary for discussion, and introduce a benchmark built on the task of Reconnaissance Blind TicTacToe. On this benchmark, we show that our policy outperforms policies ignoring the existence of multiple modes. Comment: 5 pages, 3 figures, 1 table. Submitted to the 2nd International Conference on Computing and Machine Intelligence (ICMI-22)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.11737 Zobrazit plný text záznamu View this record from Arxiv