Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response

Hyun-Rok Lee and Taesik Lee

European Journal of Operational Research, 2021, vol. 291, issue 1, 296-308

Abstract: Disaster response operations typically involve multiple decision-makers, and each decision-maker needs to make its decisions given only incomplete information on the current situation. To account for these characteristics – decision making by multiple decision-makers with partial observations to achieve a shared objective –, we formulate the decision problem as a decentralized-partially observable Markov decision process (dec-POMDP) model. To tackle a well-known difficulty of optimally solving a dec-POMDP model, multi-agent reinforcement learning (MARL) has been used as a solution technique. However, typical MARL algorithms are not always effective to solve dec-POMDP models. Motivated by evidence in single-agent RL cases, we propose a MARL algorithm augmented by pretraining. Specifically, we use behavioral cloning (BC) as a means to pretrain a neural network. We verify the effectiveness of the proposed method by solving a dec-POMDP model for a decentralized selective patient admission problem. Experimental results of three disaster scenarios show that the proposed method is a viable solution approach to solving dec-POMDP problems and that augmenting MARL with BC for its pretraining seems to offer advantages over plain MARL in terms of solution quality and computation time.

Keywords: OR in disaster relief; Artificial intelligence; Multi-agent reinforcement learning; Imitation learning; Selective patient admission (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221720308043
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:291:y:2021:i:1:p:296-308

DOI: 10.1016/j.ejor.2020.09.018

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().