Improving Reinforcement Learning by Using Case Based Heuristics

Autor(es)

Bianchi, Reinaldo A. C. ; Ros, Raquel ; Lopez de Mantaras, Ramon ; McGinty, L ; Wilson, DC

• Tipo de documento: Artigo Científico
• Fonte: Lecture Notes in Computer Science; v. 5650, p. 3-pg., 2009-01-01.

Resumo

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HARL is a subset of RL that makes use of a heuristic function derived from a case base, in a Case Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q-Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods. (AU)

• Processo FAPESP: 09/01610-1 - Desenvolvimento de Estratégia de Controle baseadas em Inteligência de Enxames aplicadas a Futebol de Robôs
• Beneficiário: Danilo Tadashi Doi
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica