Multi-agent Neural Reinforcement-Learning System with Communication

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

Autores

David Simões (Autor) Outra; Nuno Lau (Autor) Outra; Luís Paulo Reis (Autor) FEUP

Ata de Conferência Internacional

Título: Advances in Intelligent Systems and Computing

Páginas: 3-12

World Conference on Information Systems and Technologies, WorldCIST 2019

16 April 2019 through 19 April 2019

Indexação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00Q-K9P

DOI: 10.1007/978-3-030-16184-2_1

Abstract (EN): Deep learning models have as of late risen as popular function approximators for single-agent reinforcement learning challenges, by accurately estimating the value function of complex environments and being able to generalize to new unseen states. For multi-agent fields, agents must cope with the non-stationarity of the environment, due to the presence of other agents, and can take advantage of information sharing techniques for improved coordination. We propose an neural-based actor-critic algorithm, which learns communication protocols between agents and implicitly shares information during the learning phase. Large numbers of agents communicate with a self-learned protocol during distributed execution, and reliably learn complex strategies and protocols for partially observable multi-agent environments. © Springer Nature Switzerland AG 2019.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10