Tuning-free step-size adaptation. - Ashique Rupam Mahmood; Richard S. Sutton; Thomas Degris; Patrick M. Pilarski (ICASSP, 2012)

Representation Search through Generate and Test. - Ashique Rupam Mahmood; Richard S. Sutton (AAAI Workshop: Learning Rich Representations from Low-Level Sensors, 2013)

Position Paper: Representation Search through Generate and Test. - Ashique Rupam Mahmood; Richard S. Sutton (SARA, 2013)

A new Q(lambda) with interim forward view and Monte Carlo equivalence. - Richard S. Sutton; Ashique Rupam Mahmood; Doina Precup; Hado van Hasselt (ICML, 2014)

Weighted importance sampling for off-policy learning with linear function approximation. - Ashique Rupam Mahmood; Hado van Hasselt; Richard S. Sutton (NIPS, 2014)

Off-policy TD( l) with a true online equivalence. - Hado van Hasselt; Ashique Rupam Mahmood; Richard S. Sutton (UAI, 2014)

Off-policy learning based on weighted importance sampling with linear computational complexity. - Ashique Rupam Mahmood; Richard S. Sutton (UAI, 2015)

Emphatic Temporal-Difference Learning. - Ashique Rupam Mahmood; Huizhen Yu; Martha White; Richard S. Sutton (CoRR, 2015)

An Empirical Evaluation of True Online TD(λ). - Harm van Seijen; Ashique Rupam Mahmood; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2015)

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)

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)

Multi-step Off-policy Learning Without Importance Sampling Ratios. - Ashique Rupam Mahmood; Huizhen Yu; Richard S. Sutton (CoRR, 2017)

Benchmarking Reinforcement Learning Algorithms on Real-World Robots. - A. Rupam Mahmood; Dmytro Korenkevych; Gautham Vasan; William Ma; James Bergstra (CoRL, 2018)

Setting up a Reinforcement Learning Task with a Real-World Robot. - A. Rupam Mahmood; Dmytro Korenkevych; Brent J. Komer; James Bergstra (IROS, 2018)

On Generalized Bellman Equations and Temporal-Difference Learning. - Huizhen Yu; Ashique Rupam Mahmood; Richard S. Sutton (J. Mach. Learn. Res., 2018)

Autoregressive Policies for Continuous Control Deep Reinforcement Learning. - Dmytro Korenkevych; A. Rupam Mahmood; Gautham Vasan; James Bergstra (IJCAI, 2019)

Heteroscedastic Uncertainty for Robust Generative Latent Dynamics. - Oliver Limoyo; Bryan Chan 002; Filip Maric; Brandon Wagstaff; A. Rupam Mahmood; Jonathan Kelly (IEEE Robotics Autom. Lett., 2020)

Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control. - Michael Przystupa; Masood Dehghan; Martin Jägersand; A. Rupam Mahmood (ICRA, 2021)

Model-free Policy Learning with Reward Gradients. - Qingfeng Lan; A. Rupam Mahmood (CoRR, 2021)

Continual Backprop: Stochastic Gradient Descent with Persistent Randomness. - Shibhansh Dohare; A. Rupam Mahmood; Richard S. Sutton (CoRR, 2021)

An Alternate Policy Gradient Estimator for Softmax Policies. - Shivam Garg 006; Samuele Tosatto; Yangchen Pan; Martha White; Rupam Mahmood (AISTATS, 2022)

Model-free Policy Learning with Reward Gradients. - Qingfeng Lan; Samuele Tosatto; Homayoon Farrahi; Rupam Mahmood (AISTATS, 2022)

A Temporal-Difference Approach to Policy Gradient Estimation. - Samuele Tosatto; Andrew Patterson; Martha White; Rupam Mahmood (ICML, 2022)

Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots. - Yufeng Yuan; A. Rupam Mahmood (ICRA, 2022)

Memory-efficient Reinforcement Learning with Knowledge Consolidation. - Qingfeng Lan; Yangchen Pan; Jun Luo 009; A. Rupam Mahmood (CoRR, 2022)

HesScale: Scalable Computation of Hessian Diagonals. - Mohamed Elsayed 003; A. Rupam Mahmood (CoRR, 2022)

Variable-Decision Frequency Option Critic. - Amirmohammad Karimi; Jun Jin 001; Jun Luo 009; A. Rupam Mahmood; Martin Jägersand; Samuele Tosatto (CoRR, 2022)

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences. - Alan Chan 001; Hugo Silva; Sungsu Lim; Tadashi Kozuno; A. Rupam Mahmood; Martha White (J. Mach. Learn. Res., 2022)

Correcting discount-factor mismatch in on-policy policy gradient methods. - Fengdi Che; Gautham Vasan; A. Rupam Mahmood (ICML, 2023)

Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers. - Yan Wang; Gautham Vasan; A. Rupam Mahmood (ICRA, 2023)

Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization. - Homayoon Farrahi; A. Rupam Mahmood (IJCNN, 2023)

Dynamic Decision Frequency with Continuous Options. - Amirmohammad Karimi; Jun Jin 001; Jun Luo 009; A. Rupam Mahmood; Martin Jägersand; Samuele Tosatto (IROS, 2023)

Loosely consistent emphatic temporal-difference learning. - Jiamin He; Fengdi Che; Yi Wan; A. Rupam Mahmood (UAI, 2023)

Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning. - Mohamed Elsayed 003; 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)

Elephant Neural Networks: Born to Be a Continual Learner. - Qingfeng Lan; A. Rupam Mahmood (CoRR, 2023)

Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation. - Qingfeng Lan; Yangchen Pan; Jun Luo 009; A. Rupam Mahmood (Trans. Mach. Learn. Res., 2023)

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 (AAMAS, 2024)

Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning. - Mohamed Elsayed 003; A. Rupam Mahmood (ICLR, 2024)

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. - Haque Ishfaq; Qingfeng Lan; Pan Xu 002; A. Rupam Mahmood; Doina Precup; Anima Anandkumar; Kamyar Azizzadenesheli (ICLR, 2024)

Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning. - Mohamed Elsayed 003; Homayoon Farrahi; Felix Dangel; A. Rupam Mahmood (ICML, 2024)

Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. - Fengdi Che; Chenjun Xiao; Jincheng Mei; Bo Dai 001; Ramki Gummadi; Oscar A. Ramirez; Christopher K. Harris; A. Rupam Mahmood; Dale Schuurmans (ICML, 2024)

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers. - Gautham Vasan; Mohamed Elsayed 003; Seyed Alireza Azimi; Jiamin He; Fahim Shahriar; Colin Bellinger; Martha White; Rupam Mahmood (NeurIPS, 2024)

Weight Clipping for Deep Continual and Reinforcement Learning. - Mohamed Elsayed 003; Qingfeng Lan; Clare Lyle; A. Rupam Mahmood (RLJ, 2024)

More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. - Haque Ishfaq; Yixin Tan; Yu Yang 001; Qingfeng Lan; Jianfeng Lu; A. Rupam Mahmood; Doina Precup; Pan Xu 002 (RLJ, 2024)

Learning to Optimize for Reinforcement Learning. - Qingfeng Lan; A. Rupam Mahmood; Shuicheng Yan; Zhongwen Xu (RLJ, 2024)

Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning. - Gautham Vasan; Yan Wang; Fahim Shahriar; James Bergstra; Martin Jägersand; A. Rupam Mahmood (RLJ, 2024)

Streaming Deep Reinforcement Learning Finally Works. - Mohamed Elsayed 003; Gautham Vasan; A. Rupam Mahmood (CoRR, 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., 2024)

AVG-DICE: Stationary Distribution Correction by Regression. - Fengdi Che; Bryan Chan; Chen Ma; A. Rupam Mahmood (CoRR, 2025)

Distribution Parameter Actor-Critic: Shifting the Agent-Environment Boundary for Diverse Action Spaces. - Jiamin He; A. Rupam Mahmood; Martha White (CoRR, 2025)

Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection. - Huiyi Wang; Fahim Shahriar; Alireza Azimi; Gautham Vasan; Rupam Mahmood; Colin Bellinger (CoRR, 2025)