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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
verfasst von : Jan Ramon, Kurt Driessens, Tom Croonenborghs
Erschienen in: Machine Learning: ECML 2007
Verlag: Springer Berlin Heidelberg
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We investigate the relation between transfer learning in reinforcement learning with function approximation and supervised learning with concept drift. We present a new incremental relational regression tree algorithm that is capable of dealing with concept drift through tree restructuring and show that it enables a Q-learner to transfer knowledge from one task to another by recycling those parts of the generalized Q-function that still hold interesting information for the new task. We illustrate the performance of the algorithm in several experiments.