Conference item
Black-box policy search with probabilistic programs
- Abstract:
-
In this work we show how to represent policies as programs: that is, as stochastic simulators with tunable parameters. To learn the parameters of such policies we develop connections between black box variational inference and existing policy search approaches. We then explain how such learning can be implemented in a probabilistic programming system. Using our own novel implementation of such a system we demonstrate both conciseness of policy representation and automatic policy parameter lea...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Host title:
- Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics, May 09-11, 2016, Cadiz, Spain
- Journal:
- Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics Journal website
- Pages:
- 1195-1204
- Publication date:
- 2016-05-09
- Acceptance date:
- 2016-05-08
- Event location:
- Barcelona
- EISSN:
-
1533-7928
- ISSN:
-
1532-4435
Item Description
- Pubs id:
-
pubs:686827
- UUID:
-
uuid:3a9151ae-c649-4b5f-bc81-afcf55b27eb6
- Local pid:
- pubs:686827
- Source identifiers:
-
686827
- Deposit date:
- 2017-03-23
Terms of use
- Copyright holder:
- Copyright 2016 Van De Meent, et al
- Copyright date:
- 2016
- Notes:
- This is an Open Access article published under a Creative Commons license, see: https://creativecommons.org/licenses/by/4.0/
- Licence:
- CC Attribution (CC BY)
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