Learning options for an MDP from demonstrations

Tamassia, M; Zambetta, F; Raffe, W; Li, X

Learning options for an MDP from demonstrations

Tamassia, M Zambetta, F Raffe, W

Li, X

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 8955 pp. 226 - 242
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Tamassia, M	en_US
dc.contributor.author	Zambetta, F	en_US
dc.contributor.author	Raffe, W https://orcid.org/0000-0001-5310-0943	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 8955 pp. 226 - 242	en_US
dc.identifier.isbn	9783319148021	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121193
dc.description.abstract	© Springer International Publishing Switzerland 2015. The options framework provides a foundation to use hierarchical actions in reinforcement learning. An agent using options, along with primitive actions, at any point in time can decide to perform a macro-action made out of many primitive actions rather than a primitive action. Such macro-actions can be hand-crafted or learned. There has been previous work on learning them by exploring the environment. Here we take a different perspective and present an approach to learn options from a set of experts demonstrations. Empirical results are also presented in a similar setting to the one used in other works in this area.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Learning options for an MDP from demonstrations	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8955	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8955	en_US

Abstract:

© Springer International Publishing Switzerland 2015. The options framework provides a foundation to use hierarchical actions in reinforcement learning. An agent using options, along with primitive actions, at any point in time can decide to perform a macro-action made out of many primitive actions rather than a primitive action. Such macro-actions can be hand-crafted or learned. There has been previous work on learning them by exploring the environment. Here we take a different perspective and present an approach to learn options from a set of experts demonstrations. Empirical results are also presented in a similar setting to the one used in other works in this area.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121193