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Transfer Learning via Multiple Inter-task Mappings
[chapter]
2012
Lecture Notes in Computer Science
In this paper we investigate using multiple mappings for transfer learning in reinforcement learning tasks. We propose two different transfer learning algorithms that are able to manipulate multiple inter-task mappings for both model-learning and model-free reinforcement learning algorithms. Both algorithms incorporate mechanisms to select the appropriate mappings, helping to avoid the phenomenon of negative transfer. The proposed algorithms are evaluated in the Mountain Car and Keepaway
doi:10.1007/978-3-642-29946-9_23
fatcat:jvczkhx6sbacbgesvkbqfkhuda