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

Reinaldo A.C. Bianchi, Paulo E. Santos, Isaac J. da Silva, Luiz A. Celiberto, Ramon Lopez de Mantaras

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)301-312
Number of pages12
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume91
Issue number2
Early online date30 Oct 2017
DOIs
Publication statusPublished - Aug 2018
Externally publishedYes

Keywords

  • Case-based reasoning
  • Reinforcement learning
  • Robotics
  • Transfer learning

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