MULTI AGENT DEEP LEARNING WITH COOPERATIVE COMMUNICATION

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Simoes, D (Autor) Outra; lau, n (Autor) Outra; reis, lp (Autor) FEUP

Revista

Título: Journal of Artificial Intelligence and Soft Computing Research

Vol. 10

Páginas: 189-207

ISSN: 2083-2567

Editora: Walter De Gruyter

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00T-BSB

DOI: 10.2478/jaiscr-2020-0013

Abstract (EN): We consider the problem of multi agents cooperating in a partially-observable environment. Agents must learn to coordinate and share relevant information to solve the tasks successfully. This article describes Asynchronous Advantage Actor-Critic with Communication (A3C2), an end-to-end differentiable approach where agents learn policies and communication protocols simultaneously. A3C2 uses a centralized learning, distributed execution paradigm, supports independent agents, dynamic team sizes, partially-observable environments, and noisy communications. We compare and show that A3C2 outperforms other state-of-the-art proposals in multiple environments.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19