MARTINS, M. F.Reinaldo Bianchi2022-01-122022-01-122014MARTINS, M. F.; BIANCHI, R. Heuristically-accelerated reinforcement learning: A comparative analysis of performance. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 15-27, 2014.1611-3349https://repositorio.fei.edu.br/handle/FEI/4047This paper presents a comparative analysis of three Reinforcement Learning algorithms (Q-learning, Q(λ)-learning and QS-learning) and their heuristically-accelerated variants (HAQL, HAQ(λ) and HAQS) where heuristics bias action selection, thus speeding up the learning. The experiments were performed in a simulated robot soccer environment which reproduces the conditions of a real competition league environment. The results clearly demonstrate that the use of heuristics substantially improves the performance of the learning algorithms. © 2014 Springer-Verlag.Acesso RestritoArtigo de evento10.1007/978-3-662-43645-5_2HeuristicsReinforcement learningRobot soccer