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  Bayes-Adaptive Simulation-based Search with Value Function Approximation

Guez, A., Heess, N., Silver, D., & Dayan, P. (2015). Bayes-Adaptive Simulation-based Search with Value Function Approximation. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (NIPS 2014) (pp. 451-459). Red Hook, NY, USA: Curran.

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 Creators:
Guez, A, Author
Heess, N, Author
Silver, D, Author
Dayan, P1, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Bayes-adaptive planning offers a principled solution to the exploration-exploitation trade-off under model uncertainty. It finds the optimal policy in belief space, which explicitly accounts for the expected effect on future rewards of reductions in uncertainty. However, the Bayes-adaptive solution is typically intractable in domains with large or continuous state spaces. We present a tractable method for approximating the Bayes-adaptive solution by combining simulation-based search with a novel value function approximation technique that generalises over belief space. Our method outperforms prior approaches in both discrete bandit tasks and simple continuous navigation and control tasks.

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 Dates: 2015
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
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Title: Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014)
Place of Event: Montreal, Canada
Start-/End Date: 2014-12-08 - 2014-12-13

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Title: Advances in Neural Information Processing Systems 27 (NIPS 2014)
Source Genre: Proceedings
 Creator(s):
Ghahramani, Z, Editor
Welling, M, Editor
Cortes, C, Editor
Lawrence, ND, Editor
Weinberger, KQ, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 451 - 459 Identifier: ISBN: 978-1-5108-0041-0