Abstract
Existing reinforcement learning approaches are often hampered by learning tabula rasa. Transfer for reinforcement learning tackles this problem by enabling the reuse of previously learned results, but may require an inter-task mapping to encode how the previously learned task and the new task are related. This paper presents an autonomous framework for learning inter-task mappings based on an adaptation of restricted Boltzmann machines. Both a full model and a computationally efficient factored model are introduced and shown to be effective in multiple transfer learning scenarios.
Original language | English |
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Title of host publication | Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II |
Editors | H. Blockeel, K. Kersting, S. Nijssen, F. Zelezny |
Place of Publication | Berlin |
Publisher | Springer |
Pages | 449-464 |
ISBN (Print) | 978-3-642-40990-5 |
DOIs | |
Publication status | Published - 2013 |
Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 23-27, 2013, Prague, Czech Republic - Prague, Czech Republic Duration: 23 Sep 2013 → 27 Sep 2013 http://www.ecmlpkdd2013.org/ |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 8189 |
ISSN (Print) | 0302-9743 |
Conference
Conference | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 23-27, 2013, Prague, Czech Republic |
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Abbreviated title | ECMLPKDD 2013 |
Country/Territory | Czech Republic |
City | Prague |
Period | 23/09/13 → 27/09/13 |
Internet address |