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Author: Victor Ulisses Pugliese

Affiliation: Federal University of São Paulo, Avenida Cesare Mansueto Giulio Lattes, 1201, São José dos Campos, Brazil

Keyword(s): Reinforcement Learning, Proximal Policy Optimization, Curriculum Learning, Video Games.

Abstract: We conducted an investigative study of Policy Gradient methods using Curriculum Learning applied in Video Games, as professors at the Federal University of Goiás created a customized SoccerTwos environment to evaluate the Machine Learning agents of students in a Reinforcement Learning course. We employed the PPO and SAC as state-of-arts in on-policy and off-policy contexts, respectively. Also, the Curriculum could improve the performance based on it is easier to teach people in a complex gradual order than randomly. So, combining them, we propose our agents win more matches than their adversaries. We measured the results by minimum, maximum, mean rewards, and the mean length per episode in checkpoints. Finally, PPO achieved the best result with Curriculum Learning, modifying players’ (position and rotation) and ball’s (speed and position) settings in time intervals. Also, It used fewer training hours than other experiments.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Pugliese, V. U. (2022). An ML Agent using the Policy Gradient Method to win a SoccerTwos Game. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-569-2; ISSN 2184-4992, SciTePress, pages 628-633. DOI: 10.5220/0011108400003179

@conference{iceis22,
author={Victor Ulisses Pugliese},
title={An ML Agent using the Policy Gradient Method to win a SoccerTwos Game},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2022},
pages={628-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011108400003179},
isbn={978-989-758-569-2},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - An ML Agent using the Policy Gradient Method to win a SoccerTwos Game
SN - 978-989-758-569-2
IS - 2184-4992
AU - Pugliese, V.
PY - 2022
SP - 628
EP - 633
DO - 10.5220/0011108400003179
PB - SciTePress