Título: Enhancing Market -Driven Multi -agent Systems with Deep Reinforcement Learning: A Hierarchical Neuro -Fuzzy Approach
Autores: Leofome, Marco Aurelio Pacheco, Harold Dias de Mello Junior, Manoela Kohler, Evelyn Batista & Alvaro Talavera
Resumo: This study presents an enhancement of the Market-Driven Multi-agent Reinforcement Learning Hierarchical Neuro-Fuzzy Model (MA-RL-HNFP-MD) by incorporating Deep Reinforcement Learning (DRL) techniques. The modified framework, referred to as the Deep Reinforcement Learning Market-Driven Hierarchical Neuro-Fuzzy Model (DRL-MD-HNFP), utilizes advanced neural network architectures to improve learning efficiency and scalability in complex multi-agent environments. Experimental results demonstrate significant performance improvements compared to the original MA-RL-HNFP-MD model, particularly in reducing task completion times and optimizing resource allocation. We validated the proposed model in benchmark scenarios, including the pursuit game and robotic soccer simulations, where it exhibited superior adaptability and coordination among agents. These findings highlight the potential of DRL-based approaches to enhance decision-making and coordination in multi-agent systems, providing valuable insights for applications in dynamic and resource-intensive environments.
Palavras-chave: component; formatting; style; styling; insert.
Páginas: 5
Código DOI: 10.21528/CBIC2025-1191043
Artigo em PDF: CBIC_2025_paper1191043.pdf
Arquivo BibTeX:
CBIC_2025_1191043.bib