Transferring knowledge as heuristics in reinforcement learning: A case-based approach

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Bianchi R.A.C.
Celiberto L.A.
Santos P.E.
Matsuura J.P.
Lopez De Mantaras R.
Artificial Intelligence
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BIANCHI, REINALDO A.C.; JUNIOR, LUIZ A. CELIBERTO; Santos, Paulo E.; MATSUURA, JACKSON P.; LÓPEZ DE MÀNTARAS, RAMÓN. Transferring knowledge as heuristics in reinforcement learning: a case-based approach. Artificial Intelligence (General Ed.), v. 226, p. 102-121, 2015.
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© 2015 Elsevier B.V.Abstract The goal of this paper is to propose and analyse a transfer learning meta-algorithm that allows the implementation of distinct methods using heuristics to accelerate a Reinforcement Learning procedure in one domain (the target) that are obtained from another (simpler) domain (the source domain). This meta-algorithm works in three stages: first, it uses a Reinforcement Learning step to learn a task on the source domain, storing the knowledge thus obtained in a case base; second, it does an unsupervised mapping of the source-domain actions to the target-domain actions; and, third, the case base obtained in the first stage is used as heuristics to speed up the learning process in the target domain. A set of empirical evaluations were conducted in two target domains: the 3D mountain car (using a learned case base from a 2D simulation) and stability learning for a humanoid robot in the Robocup 3D Soccer Simulator (that uses knowledge learned from the Acrobot domain). The results attest that our transfer learning algorithm outperforms recent heuristically-accelerated reinforcement learning and transfer learning algorithms.