Learning high-level robotic soccer strategies from scratch through reinforcement learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Miguel Abreu (Autor) Outra; Luís Paulo Reis (Autor) FEUP; Henrique Lopes Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: 2019 19TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC 2019)

Páginas: 128-134

19th IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Gondomar, PORTUGAL, APR 24-26, 2019

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00Q-TAJ

DOI: 10.1109/icarsc.2019.8733606

Abstract (EN): The field of automated learning has been steadily growing in robotic tasks. This phenomenon is supported by the evolution of computational resources and new reinforcement learning algorithms. Researchers have drawn their attentions to methods that are easy to implement and tune, while achieving state-of-the-art performance. This trend also affects the world of robotic soccer, where new papers delve systematically into the optimization of basic skills. However, when learning higher-level strategies, there is space for improvement on two fronts. First, the simulation environment should allow the agent to abstract from low-level details. Second, the existing methods to train this kind of behaviors are still scarce. This paper contributes with innovative problem-solving methods, specifically in the rewards field. To test alternative approaches, an extended version of the RoboCup's official Soccer Server simulator was used. The results have confirmed the importance of the proposed reward components and their relationship with the episodes' initial conditions.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7