56 citations · 182 across the 18 of their papers we have counts for
19 papers
Correcting discount-factor mismatch in on-policy policy gradient methods
Fengdi Che, Gautham Vasan, A. Rupam Mahmood
The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution…
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq, Qingfeng Lan, Pan Xu +4
We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling algor…
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning
Mohamed Elsayed, A. Rupam Mahmood
Modern representation learning methods often struggle to adapt quickly under non-stationarity because they suffer from catastrophic forgetting and decaying plasticity. Such problem…
Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi, Jun Jin, Jun Luo +3
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as settin…
HesScale: Scalable Computation of Hessian Diagonals
Mohamed Elsayed, A. Rupam Mahmood
Second-order optimization uses curvature information about the objective function, which can help in faster convergence. However, such methods typically require expensive computati…
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers
Yan Wang, Gautham Vasan, A. Rupam Mahmood
Real-time learning is crucial for robotic agents adapting to ever-changing, non-stationary environments. A common setup for a robotic agent is to have two different computers simul…