FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs
Abstract
Planning in partially observable Markov decision processes (POMDPs) remains a challenging topic in the artificial intelligence community, in spite of recent impressive progress in approximation techniques. Previous research has indicated that online planning approaches are promising in handling large-scale POMDP domains efficiently as they make decisions "on demand" instead of proactively for the entire state space. We present a Factored Hybrid Heuristic Online Planning (FHHOP) algorithm for large POMDPs. FHHOP gets its power by combining a novel hybrid heuristic search strategy with a recently developed factored state representation. On several benchmark problems, FHHOP substantially outperformed state-of-the-art online heuristic search approaches in terms of both scalability and quality.
Cite
@article{arxiv.1210.4912,
title = {FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs},
author = {Zhongzhang Zhang and Xiaoping Chen},
journal= {arXiv preprint arXiv:1210.4912},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)