Ferreira L.A.Costa Ribeiro C.H.Da Costa Bianchi R.A.2019-08-192019-08-192014FERREIRA, LEONARDO ANJOLETTO; COSTA RIBEIRO, CARLOS HENRIQUE; DA COSTA BIANCHI, REINALDO AUGUSTO. Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems. Applied Intelligence (Boston), v. 1, p. 1-12, 2014.0924-669Xhttps://repositorio.fei.edu.br/handle/FEI/1241This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a heuristic function applied in standard Reinforcement Learning algorithms to simplify and speed up the learning process of an agent that learns in a multi-agent multi-objective environment. In order to verify performance of the proposed algorithms, we considered a predator-prey environment in which the learning agent plays the role of prey that must escape the pursuing predator while reaching for food in a fixed location. The results show that combining modularization and acceleration using a heuristics function indeed produced simplification and speeding up of the learning process in a complex problem when comparing with algorithms that do not make use of acceleration or modularization techniques, such as Q-Learning and Minimax-Q. © 2014 Springer Science+Business Media New York.Acesso RestritoArtigo10.1007/s10489-014-0534-0Heuristically accelerated reinforcement learningMulti-agent systemsMulti-objective problemsReinforcement learning