Title: Transfer learning for reinforcement learning through goal and policy parametrization
Authors: Driessens, Kurt ×
Ramon, Jan
Croonenborghs, Tom #
Issue Date: 2006
Host Document: Proceedings of the ICML Workshop on Structural Knowledge Transfer for Machine Learning (Online Proceedings) pages:1-4
Conference: ICML Workshop on Structural Knowledge Transfer for Machine Learning location:Pittsburgh, Pennsylvania, USA date:June 29, 2006
Abstract: Relational reinforcement learning has allowed results from reinforcement learning tasks to be re-used in other, closely related, tasks. This transfer of knowledge is made possible by the use of parameters in the representations of the task-description and the learned policy. In this paper, we will give a description of the current state of the art of transfer learning with relational reinforcement learning, make some observations about the usefulness and limitations of this current state and discuss some directions for future research. We also present a first small step along one of those directions.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
Technologiecluster Computerwetenschappen
Computer Science Technology TC, Technology Campus Geel
× corresponding author
# (joint) last author

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