Author of the publication

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

Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning.

S. Sharma, A. Suresh, R. Ramesh, and B. Ravindran. CoRR, (2017)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Rahul Ramesh Harshe

Rahul Gupta

Ramesh Ummanni

Ramesh Boggula

Rahul Dewan

Other publications of authors with the same name

Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model.M. Khona, M. Okawa, J. Hula, R. Ramesh, K. Nishi, R. Dick, E. Lubana, and H. Tanaka. CoRR, (2024)The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold.J. Mao, I. Griniasty, H. Teoh, R. Ramesh, R. Yang, M. Transtrum, J. Sethna, and P. Chaudhari. CoRR, (2023)Boosting a Model Zoo for Multi-Task and Continual Learning.R. Ramesh, and P. Chaudhari. CoRR, (2021)A picture of the space of typical learnable tasks.R. Ramesh, J. Mao, I. Griniasty, R. Yang, H. Teoh, M. Transtrum, J. Sethna, and P. Chaudhari. CoRR, (2022)The Value of Out-of-Distribution Data.A. Silva, R. Ramesh, C. Priebe, P. Chaudhari, and J. Vogelstein. ICML, volume 202 of Proceedings of Machine Learning Research, page 7366-7389. PMLR, (2023)Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning.S. Sharma, A. Suresh, R. Ramesh, and B. Ravindran. CoRR, (2017)A Picture of the Space of Typical Learnable Tasks.R. Ramesh, J. Mao, I. Griniasty, R. Yang, H. Teoh, M. Transtrum, J. Sethna, and P. Chaudhari. ICML, volume 202 of Proceedings of Machine Learning Research, page 28680-28700. PMLR, (2023)Deep Reference Priors: What is the best way to pretrain a model?Y. Gao, R. Ramesh, and P. Chaudhari. ICML, volume 162 of Proceedings of Machine Learning Research, page 7036-7051. PMLR, (2022)Influence of learners' motivation on responses to Facebook promotions of online courses.R. Ramesh, and D. Gupta. ICACCI, page 1728-1733. IEEE, (2015)Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning.A. Manoharan, R. Ramesh, and B. Ravindran. ECML/PKDD (2), volume 12458 of Lecture Notes in Computer Science, page 509-524. Springer, (2020)

BibSonomy

Disambiguation of "Ramesh, Rahul"

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

Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning.

Please choose a person to relate this publication to

Rahul Ramesh Harshe

Rahul Gupta

Ramesh Ummanni

Ramesh Boggula

Rahul Dewan

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Ramesh, Rahul"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning.

Please choose a person to relate this publication to

Rahul Ramesh Harshe

Rahul Gupta

Ramesh Ummanni

Ramesh Boggula

Rahul Dewan

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
Harvard
MSOffice XML

Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning.