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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

A. Barreto, D. Borsa, J. Quan, T. Schaul, D. Silver, M. Hessel, D. Mankowitz, A. Zídek, and R. Munos. ICML, volume 80 of Proceedings of Machine Learning Research, page 510-519. PMLR, (2018)

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Tom Schaul

Marieta Ioana Toma

Zentrosomenmorphologie in Prostatakarzinomen: eine immunhistologische Studie mit Korrelation zu DNA-Ploidie und kliniko-pathologischen ParameternM. Toma. TU Dresden, (2008)

Tom Ring

Tom Garven

Tom Butters

Other publications of authors with the same name

No More Pesky Learning RatesT. Schaul, S. Zhang, and Y. LeCun. CoRR, (2012)The two-dimensional organization of behavior.M. Ring, T. Schaul, and J. Schmidhuber. ICDL-EPIROB, page 1-8. IEEE, (2011)Universal Value Function Approximators.T. Schaul, D. Horgan, K. Gregor, and D. Silver. ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1312-1320. JMLR.org, (2015)Q-Error as a Selection Mechanism in Modular Reinforcement-Learning Systems.M. Ring, and T. Schaul. IJCAI, page 1452-1457. IJCAI/AAAI, (2011)Return-based Scaling: Yet Another Normalisation Trick for Deep RL.T. Schaul, G. Ostrovski, I. Kemaev, and D. Borsa. CoRR, (2021)Better Generalization with Forecasts.T. Schaul, and M. Ring. IJCAI, page 1656-1662. IJCAI/AAAI, (2013)Universal Successor Features Approximators.D. Borsa, A. Barreto, J. Quan, D. Mankowitz, H. van Hasselt, R. Munos, D. Silver, and T. Schaul. ICLR (Poster), OpenReview.net, (2019)The Predictron: End-To-End Learning and Planning.D. Silver, H. van Hasselt, M. Hessel, T. Schaul, A. Guez, T. Harley, G. Dulac-Arnold, D. Reichert, N. Rabinowitz, A. Barreto and 1 other author(s). ICML, volume 70 of Proceedings of Machine Learning Research, page 3191-3199. PMLR, (2017)Dueling Network Architectures for Deep Reinforcement Learning.Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 1995-2003. JMLR.org, (2016)Dueling Network Architectures for Deep Reinforcement LearningZ. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. (2015)cite arxiv:1511.06581Comment: 15 pages, 5 figures, and 5 tables.

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Disambiguation of "Schaul, Tom"

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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

Please choose a person to relate this publication to

Tom Schaul

Marieta Ioana Toma

Tom Ring

Tom Garven

Tom Butters

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Schaul, Tom"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

Please choose a person to relate this publication to

Tom Schaul

Marieta Ioana Toma

Tom Ring

Tom Garven

Tom Butters

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.