AAAI Publications, 2013 AAAI Spring Symposium Series

Font Size: 
Autonomous Selection of Inter-Task Mappings in Transfer Learning (extended abstract)
Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis Vlahavas

Last modified: 2013-03-15


When transferring knowledge between reinforcement learning agents with different state representations or actions, past knowledge must be efficiently mapped so that it assists learning. The majority of the existing approaches use pre-defined mappings given by a domain expert. To overcome this limitations and allow autonomous transfer learning, this paper introduces a method for weighting and using multiple inter-task mappings, named COMBREL. Experimental results show that the use of multiple inter-task mappings, accompanied with a selection mechanism, can significantly boost the performance of transfer learning, relative to learning without transfer and relative to using a single hand-picked mapping.


machine learning, reinforcement learning, transfer learning

Full Text: PDF