A. Rupam Mahmood

According to our database, A. Rupam Mahmood authored at least 48 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Loss of plasticity in deep continual learning.

Shibhansh Dohare, J. Fernando Hernandez-Garcia, Qingfeng Lan, Parash Rahman, A. Rupam Mahmood, Richard S. Sutton

Nat., August, 2024

Streaming Deep Reinforcement Learning Finally Works.

Mohamed Elsayed, Gautham Vasan, A. Rupam Mahmood

CoRR, 2024

Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning.

Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood

RLJ, 2024

Learning to Optimize for Reinforcement Learning.

Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu

RLJ, 2024

More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling.

Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu

RLJ, 2024

Weight Clipping for Deep Continual and Reinforcement Learning.

Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood

RLJ, 2024

Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation.

Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning.

Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo.

Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning.

Mohamed Elsayed, A. Rupam Mahmood

Proceedings of the Twelfth International Conference on Learning Representations, 2024

MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning.

Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2023

Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation.

Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood

Trans. Mach. Learn. Res., 2023

Elephant Neural Networks: Born to Be a Continual Learner.

Qingfeng Lan, A. Rupam Mahmood

CoRR, 2023

Maintaining Plasticity in Deep Continual Learning.

Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood

CoRR, 2023

Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning.

Mohamed Elsayed, A. Rupam Mahmood

CoRR, 2023

Loosely consistent emphatic temporal-difference learning.

Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Dynamic Decision Frequency with Continuous Options.

Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto

IROS, 2023

Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization.

Homayoon Farrahi, A. Rupam Mahmood

Proceedings of the International Joint Conference on Neural Networks, 2023

Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers.

Yan Wang, Gautham Vasan, A. Rupam Mahmood

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Correcting discount-factor mismatch in on-policy policy gradient methods.

Fengdi Che, Gautham Vasan, A. Rupam Mahmood

Proceedings of the International Conference on Machine Learning, 2023

2022

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences.

Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White

J. Mach. Learn. Res., 2022

Variable-Decision Frequency Option Critic.

Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto

CoRR, 2022

HesScale: Scalable Computation of Hessian Diagonals.

Mohamed Elsayed, A. Rupam Mahmood

CoRR, 2022

Memory-efficient Reinforcement Learning with Knowledge Consolidation.

Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood

CoRR, 2022

Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots.

Yufeng Yuan, A. Rupam Mahmood

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

A Temporal-Difference Approach to Policy Gradient Estimation.

Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood

Proceedings of the International Conference on Machine Learning, 2022

Model-free Policy Learning with Reward Gradients.

Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, Rupam Mahmood

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

An Alternate Policy Gradient Estimator for Softmax Policies.

Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, Rupam Mahmood

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Continual Backprop: Stochastic Gradient Descent with Persistent Randomness.

Shibhansh Dohare, A. Rupam Mahmood, Richard S. Sutton

CoRR, 2021

Model-free Policy Learning with Reward Gradients.

Qingfeng Lan, A. Rupam Mahmood

CoRR, 2021

Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control.

Michael Przystupa, Masood Dehghan, Martin Jägersand, A. Rupam Mahmood

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Heteroscedastic Uncertainty for Robust Generative Latent Dynamics.

Oliver Limoyo, Bryan Chan, Filip Maric, Brandon Wagstaff, A. Rupam Mahmood, Jonathan Kelly

IEEE Robotics Autom. Lett., 2020

2019

Autoregressive Policies for Continuous Control Deep Reinforcement Learning.

Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

On Generalized Bellman Equations and Temporal-Difference Learning.

Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton

J. Mach. Learn. Res., 2018

Setting up a Reinforcement Learning Task with a Real-World Robot.

A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra

Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Benchmarking Reinforcement Learning Algorithms on Real-World Robots.

A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra

Proceedings of the 2nd Annual Conference on Robot Learning, 2018

2017

Multi-step Off-policy Learning Without Importance Sampling Ratios.

Ashique Rupam Mahmood, Huizhen Yu, Richard S. Sutton

CoRR, 2017

2016

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning.

Richard S. Sutton, Ashique Rupam Mahmood, Martha White

J. Mach. Learn. Res., 2016

True Online Temporal-Difference Learning.

Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton

J. Mach. Learn. Res., 2016

2015

An Empirical Evaluation of True Online TD(λ).

Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Richard S. Sutton

CoRR, 2015

Emphatic Temporal-Difference Learning.

Ashique Rupam Mahmood, Huizhen Yu, Martha White, Richard S. Sutton

CoRR, 2015

Off-policy learning based on weighted importance sampling with linear computational complexity.

Ashique Rupam Mahmood, Richard S. Sutton

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2014

Off-policy TD( l) with a true online equivalence.

Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Weighted importance sampling for off-policy learning with linear function approximation.

Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

A new Q(lambda) with interim forward view and Monte Carlo equivalence.

Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Position Paper: Representation Search through Generate and Test.

Ashique Rupam Mahmood, Richard S. Sutton

Proceedings of the Tenth Symposium on Abstraction, Reformulation, and Approximation, 2013

Representation Search through Generate and Test.

Ashique Rupam Mahmood, Richard S. Sutton

Proceedings of the Learning Rich Representations from Low-Level Sensors, 2013

2012

Tuning-free step-size adaptation.

Ashique Rupam Mahmood, Richard S. Sutton, Thomas Degris, Patrick M. Pilarski

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012