Heuristically accelerated Q-learning: A new approach to speed up reinforcement learning
Nenhuma Miniatura disponível
Citações na Scopus
43
Tipo de produção
Artigo
Data
2004-01-05
Autores
Reinaldo Bianchi
RIBEIRO, C. H. C.
COSTA, A. H. R.
RIBEIRO, C. H. C.
COSTA, A. H. R.
Orientador
Periódico
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Título da Revista
ISSN da Revista
Título de Volume
Citação
BIANCHI, R.; RIBEIRO, C. H. C.; COSTA, A. H. R. Heuristically accelerated Q-learning: A new approach to speed up reinforcement learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 3171, p. 245-254, 2004.
Texto completo (DOI)
Palavras-chave
Resumo
This work presents a new algorithm, called Heuristically Accelerated Q-Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q-learning. A heuristic function H that influences the choice of the actions characterizes the HAQL algorithm. The heuristic function is strongly associated with the policy: it indicates that an action must be taken instead of another. This work also proposes an automatic method for the extraction of the heuristic function H from the learning process, called Heuristic from Exploration. Finally, experimental results shows that even a very simple heuristic results in a significant enhancement of performance of the reinforcement learning algorithm. © Springer-Verlag 2004.