2019-10-15-osogami19a.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Real-time tree search with pessimistic scenarios: Winning the NeurIPS 2018 Pommerman Competition	acml19	Autonomous agents need to make decisions in a sequential manner, under partially observable environment, and in consideration of how other agents behave. In critical situations, such decisions need to be made in real time for example to avoid collisions and recover to safe conditions. We propose a technique of tree search where a deterministic and pessimistic scenario is used after a specified depth. Because there is no branching with the deterministic scenario, the proposed technique allows us to take into account the events that can occur far ahead in the future. The effectiveness of the proposed technique is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018 competition, where the agents that implement the proposed technique have won the first and third places.	inproceedings	Proceedings of Machine Learning Research	osogami19a	0	Real-time tree search with pessimistic scenarios: Winning the NeurIPS 2018 Pommerman Competition	583	598	583-598	583	false	Osogami, Takayuki and Takahashi, Toshihiro	given family Takayuki Osogami given family Toshihiro Takahashi	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/osogami19a/osogami19a.pdf