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ORCIDA. Rupam Mahmood
Person information
- affiliation: University of Alberta, Reinforcement Learning & Artificial Intelligence Lab, Edmonton, AB, Canada
- affiliation: Alberta Machine Intelligence Institute (Amii), Edmonton, AB, Canada
- affiliation: Kindred AI, Toronto, ON, Canada
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2020 – today
- 2023
[i23]Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu:
Learning to Optimize for Reinforcement Learning. CoRR abs/2302.01470 (2023)- [i22]Mohamed Elsayed, A. Rupam Mahmood:
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning. CoRR abs/2302.03281 (2023) - 2022
- [c16]Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, Rupam Mahmood:
Model-free Policy Learning with Reward Gradients. AISTATS 2022: 4217-4234 - [c15]Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, Rupam Mahmood:
An Alternate Policy Gradient Estimator for Softmax Policies. AISTATS 2022: 6630-6689 - [c14]Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood:
A Temporal-Difference Approach to Policy Gradient Estimation. ICML 2022: 21609-21632 - [c13]Yufeng Yuan, A. Rupam Mahmood:
Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots. ICRA 2022: 5546-5552 - [i21]Samuele Tosatto, Andrew Patterson, Martha White, A. Rupam Mahmood:
A Temporal-Difference Approach to Policy Gradient Estimation. CoRR abs/2202.02396 (2022) - [i20]Yufeng Yuan, Rupam Mahmood:
Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots. CoRR abs/2203.12759 (2022) - [i19]Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood:
Memory-efficient Reinforcement Learning with Knowledge Consolidation. CoRR abs/2205.10868 (2022) - [i18]Yan Wang, Gautham Vasan, A. Rupam Mahmood:
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers. CoRR abs/2210.02317 (2022) - [i17]Mohamed Elsayed, A. Rupam Mahmood:
HesScale: Scalable Computation of Hessian Diagonals. CoRR abs/2210.11639 (2022) - [i16]Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto:
Variable-Decision Frequency Option Critic. CoRR abs/2212.04407 (2022) - 2021
- [c12]Michael Przystupa, Masood Dehghan, Martin Jägersand, A. Rupam Mahmood:
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control. ICRA 2021: 14276-14283 - [i15]Qingfeng Lan, A. Rupam Mahmood:
Model-free Policy Learning with Reward Gradients. CoRR abs/2103.05147 (2021) - [i14]Michael Przystupa, Masood Dehghan, Martin Jägersand, A. Rupam Mahmood:
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control. CoRR abs/2106.06083 (2021) - [i13]Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White:
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences. CoRR abs/2107.08285 (2021) - [i12]Shibhansh Dohare, A. Rupam Mahmood, Richard S. Sutton:
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness. CoRR abs/2108.06325 (2021) - [i11]Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, A. Rupam Mahmood:
An Alternate Policy Gradient Estimator for Softmax Policies. CoRR abs/2112.11622 (2021) - 2020
- [j4]Oliver Limoyo
, Bryan Chan, Filip Maric, Brandon Wagstaff, A. Rupam Mahmood, Jonathan Kelly:
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics. IEEE Robotics Autom. Lett. 5(4): 6654-6661 (2020) - [i10]Oliver Limoyo, Bryan Chan, Filip Maric, Brandon Wagstaff, A. Rupam Mahmood, Jonathan Kelly:
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics. CoRR abs/2008.08157 (2020)
2010 – 2019
- 2019
- [c11]Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra:
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. IJCAI 2019: 2754-2762 - [i9]Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra:
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. CoRR abs/1903.11524 (2019) - 2018
- [j3]Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton:
On Generalized Bellman Equations and Temporal-Difference Learning. J. Mach. Learn. Res. 19: 48:1-48:49 (2018) - [c10]A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra:
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRL 2018: 561-591 - [c9]A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra:
Setting up a Reinforcement Learning Task with a Real-World Robot. IROS 2018: 4635-4640 - [i8]A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra:
Setting up a Reinforcement Learning Task with a Real-World Robot. CoRR abs/1803.07067 (2018) - [i7]A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra:
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRR abs/1809.07731 (2018) - 2017
- [c8]Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton:
On Generalized Bellman Equations and Temporal-Difference Learning. Canadian Conference on AI 2017: 3-14 - [i6]Ashique Rupam Mahmood, Huizhen Yu, Richard S. Sutton:
Multi-step Off-policy Learning Without Importance Sampling Ratios. CoRR abs/1702.03006 (2017) - [i5]Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton:
On Generalized Bellman Equations and Temporal-Difference Learning. CoRR abs/1704.04463 (2017) - 2016
- [j2]Richard S. Sutton, Ashique Rupam Mahmood, Martha White:
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. J. Mach. Learn. Res. 17: 73:1-73:29 (2016) - [j1]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. J. Mach. Learn. Res. 17: 145:1-145:40 (2016) - 2015
- [c7]Ashique Rupam Mahmood, Richard S. Sutton:
Off-policy learning based on weighted importance sampling with linear computational complexity. UAI 2015: 552-561 - [i4]Richard S. Sutton, Ashique Rupam Mahmood, Martha White:
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. CoRR abs/1503.04269 (2015) - [i3]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Richard S. Sutton:
An Empirical Evaluation of True Online TD(λ). CoRR abs/1507.00353 (2015) - [i2]Ashique Rupam Mahmood, Huizhen Yu, Martha White, Richard S. Sutton:
Emphatic Temporal-Difference Learning. CoRR abs/1507.01569 (2015) - [i1]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. CoRR abs/1512.04087 (2015) - 2014
- [c6]Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt:
A new Q(lambda) with interim forward view and Monte Carlo equivalence. ICML 2014: 568-576 - [c5]Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton:
Weighted importance sampling for off-policy learning with linear function approximation. NIPS 2014: 3014-3022 - [c4]Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton:
Off-policy TD( l) with a true online equivalence. UAI 2014: 330-339 - 2013
- [c3]Ashique Rupam Mahmood, Richard S. Sutton:
Representation Search through Generate and Test. AAAI Workshop: Learning Rich Representations from Low-Level Sensors 2013 - [c2]Ashique Rupam Mahmood, Richard S. Sutton:
Position Paper: Representation Search through Generate and Test. SARA 2013 - 2012
- [c1]Ashique Rupam Mahmood, Richard S. Sutton, Thomas Degris, Patrick M. Pilarski:
Tuning-free step-size adaptation. ICASSP 2012: 2121-2124
Coauthor Index
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