| Autor(es): |
Bianchi, Reinaldo A. C.
;
Ribeiro, Carlos H. C.
;
Costa, Anna H. R.
;
Veloso, MM
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022); v. N/A, p. 6-pg., 2007-01-01. |
| Resumo | |
This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the well-known Multiagent Reinforcement Learning algorithm Minimax-Q. A heuristic function H that influences the choice of the actions characterises the HAMMQ algorithm. This function is associated with a preference policy that indicates that a certain action must be taken instead of another. A set of empirical evaluations were conducted for the proposed algorithm in a simplified simulator for the robot soccer domain, and experimental results show that even very simple heuristics enhances significantly the performance of the multiagent reinforcement learning algorithm. (AU) | |
| Processo FAPESP: | 06/05667-0 - Heuristic selection of actions in multiagent reinforcement learning. |
| Beneficiário: | Reinaldo Augusto da Costa Bianchi |
| Modalidade de apoio: | Auxílio à Pesquisa - Reunião - Exterior |