Hierarchical Reinforcement Learning and Evolution Strategies for Cooperative Robotic Soccer

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Santos, B (Autor) Outra; Cardoso, A (Autor) Outra; Ledo, G (Autor) Outra; reis, lp (Autor) FEUP; Armando Jorge Sousa (Autor) FEUP

Ata de Conferência Internacional

Título: 2024 7TH IBERIAN ROBOTICS CONFERENCE, ROBOT 2024

Páginas: 1-6

7th Iberian Robotics Conference

Madrid, SPAIN, NOV 06-08, 2024

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-XRG

DOI: 10.1109/robot61475.2024.10797413

Abstract (EN): Artificial I ntelligence ( AI) a nd M achine Learning are frequently used to develop player skills in robotic soccer scenarios. Despite the potential of deep reinforcement learning, its computational demands pose challenges when learning complex behaviors. This work explores less demanding methods, namely Evolution Strategies (ES) and Hierarchical Reinforcement Learning (HRL), for enhancing coordination and cooperation between two agents from the FC Portugal 3D Simulation Soccer Team, in RoboCup. The goal is for two robots to learn a high-level skill that enables a robot to pass the ball to its teammate as quickly as possible. Results show that the trained models under-performed in a traditional robotic soccer two-agent task and scored perfectly in a much simpler one. Therefore, this work highlights that while these alternative methods can learn trivial cooperative behavior, more complex tasks are difficult t o learn.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6