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Authors: Atom Scott 1 ; Keisuke Fujii 2 and Masaki Onishi 1

Affiliations: 1 National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan ; 2 Graduate School of Informatics, Nagoya University, Nagoya, Japan

Keyword(s): Deep Reinforcement Learning, Football, Agent-Based Simulation, Network Theory.

Abstract: Recent advances in reinforcement learning (RL) have made it possible to develop sophisticated agents that excel in a wide range of applications. Simulations using such agents can provide valuable information in scenarios that are difficult to scientifically experiment in the real world. In this paper, we examine the play- style characteristics of football RL agents and uncover how strategies may develop during training. The learnt strategies are then compared with those of real football players. We explore what can be learnt from the use of simulated environments by using aggregated statistics and social network analysis (SNA). As a result, we found that (1) there are strong correlations between the competitiveness of an agent and various SNA metrics and (2) aspects of the RL agents play style become similar to real world footballers as the agent becomes more competitive. We discuss further advances that may be necessary to improve our understanding necessary to fully utilise RL for the analysis of football. (More)

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Paper citation in several formats:
Scott, A.; Fujii, K. and Onishi, M. (2022). How Does AI Play Football? An Analysis of RL and Real-world Football Strategies. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 42-52. DOI: 10.5220/0010844300003116

@conference{icaart22,
author={Atom Scott. and Keisuke Fujii. and Masaki Onishi.},
title={How Does AI Play Football? An Analysis of RL and Real-world Football Strategies},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2022},
pages={42-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010844300003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - How Does AI Play Football? An Analysis of RL and Real-world Football Strategies
SN - 978-989-758-547-0
IS - 2184-433X
AU - Scott, A.
AU - Fujii, K.
AU - Onishi, M.
PY - 2022
SP - 42
EP - 52
DO - 10.5220/0010844300003116
PB - SciTePress

- Science and Technology Publications, Lda.
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