Heuristically Accelerated Reinforcement Learning by Means of Case-Based Reasoning and Transfer Learning |
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Authors: | Reinaldo A C Bianchi Paulo E Santos Isaac J da Silva Jr" target="_blank">Luiz A CelibertoJr Ramon Lopez de Mantaras |
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Affiliation: | 1.Centro Universitario FEI,S?o Bernardo do Campo,Brazil;2.Federal University of ABC,Santo Andre,Brazil;3.Artificial Intelligence Research Institute (IIIA-CSIC),Bellaterra,Spain |
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Abstract: | 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. |
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