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Heuristically Accelerated Reinforcement Learning by Means of Case-Based Reasoning and Transfer Learning

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Autor(es):
Bianchi, Reinaldo A. C. ; Santos, Paulo E. ; da Silva, Isaac J. ; Celiberto, Luiz A., Jr. ; de Mantaras, Ramon Lopez
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS; v. 91, n. 2, p. 12-pg., 2018-08-01.
Resumo

Reinforcement Learning (RL) is a well-known technique for learning the solutions of control problems from the interactions of an agent in its domain. However, RL is known to be inefficient in problems of the real-world where the state space and the set of actions grow up fast. Recently, heuristics, case-based reasoning (CBR) and transfer learning have been used as tools to accelerate the RL process. This paper investigates a class of algorithms called Transfer Learning Heuristically Accelerated Reinforcement Learning (TLHARL) that uses CBR as heuristics within a transfer learning setting to accelerate RL. The main contributions of this work are the proposal of a new TLHARL algorithm based on the traditional RL algorithm Q(lambda) and the application of TLHARL on two distinct real-robot domains: a robot soccer with small-scale robots and the humanoid-robot stability learning. Experimental results show that our proposed method led to a significant improvement of the learning rate in both domains. (AU)

Processo FAPESP: 16/21047-3 - ALIS: Aprendizado Autônomo em Sistemas Inteligentes
Beneficiário:Anna Helena Reali Costa
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 16/18792-9 - Descrição, representação e solução de jogos espaciais
Beneficiário:Paulo Eduardo Santos
Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE