Bianchi R.A.C.Santos P.E.da Silva I.J.Celiberto L.A.Lopez de Mantaras R.2019-08-192019-08-192018Bianchi, Reinaldo A. C.; Santos, Paulo E.; DA SILVA, ISAAC J.; CELIBERTO, LUIZ A.; LOPEZ DE MANTARAS, RAMON. Heuristically Accelerated Reinforcement Learning by Means of Case-Based Reasoning and Transfer Learning. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 1, p. 1, 2017.1573-0409https://repositorio.fei.edu.br/handle/FEI/1211© 2017, Springer Science+Business Media B.V.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(λ) 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.Acesso RestritoArtigo10.1007/s10846-017-0731-2Case-based reasoningReinforcement learningRoboticsTransfer learning