Multi-agent Double Deep Q-Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

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

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)

Páginas: 123-134

18th EPIA Conference on Artificial Intelligence (EPIA)

Univ Porto, Fac Engn, Porto, PORTUGAL, SEP 05-08, 2017

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00M-YK9

DOI: 10.1007/978-3-319-65340-2_11

Abstract (EN): There are many open issues and challenges in the multi-agent reward-based learning field. Theoretical convergence guarantees are lost, and the complexity of the action-space is also exponential to the amount of agents calculating their optimal joint-action. Function approximators, such as deep neural networks, have successfully been used in singleagent environments with high dimensional state-spaces. We propose the Multi-agent Double Deep Q-Networks algorithm, an extension of Deep Q-Networks to the multi-agent paradigm. Two common techniques of multi-agent Q-learning are used to formally describe our proposal, and are tested in a Foraging Task and a Pursuit Game. We also demonstrate how they can generalize to similar tasks and to larger teams, due to the strength of deep-learning techniques, and their viability for transfer learning approaches. With only a small fraction of the initial task's training, we adapt to longer tasks, and we accelerate the task completion by increasing the team size, thus empirically demonstrating a solution to the complexity issues of the multi-agent field.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12