Towards Large Scale Ad-hoc Teamwork

Shafipour Yourdshahi, Elnaz and Pinder, Thomas and Dhawan, Gauri and Soriano Marcolino, Leandro and Angelov, Plamen Parvanov (2018) Towards Large Scale Ad-hoc Teamwork. In: 2018 IEEE International Conference on Agents (ICA). IEEE. ISBN 9781538681800

[img]
Preview
PDF (ICA2018)
ICA2018.pdf - Accepted Version

Download (598kB)

Abstract

In complex environments, agents must be able to cooperate with previously unknown team-mates, and hence dynamically learn about other agents in the environment while searching for optimal actions. Previous works employ Monte Carlo Tree Search approaches. However, the search tree increases exponentially with the number of agents, and only scenarios with very small team sizes have been explored. Hence, in this paper we propose a history-based version of UCT Monte Carlo Tree Search, using a more compact representation than the original algorithm. We perform several experiments with a varying number of agents in the level-based foraging domain, an important testbed for ad-hoc teamwork. We achieve better overall performance than the state-of-the-art and better scalability with team size. Additionally, we contribute an open-source version of our system, making it easier for the research community to use the level-based foraging domain as a benchmark problern for ad-hoc teamwork.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
126613
Deposited By:
Deposited On:
01 Aug 2018 10:12
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Sep 2020 06:59