172 citations · 431 across the 12 of their papers we have counts for
12 papers
Maintaining Plasticity in Continual Learning via Regenerative Regularization
Saurabh Kumar, Henrik Marklund, Benjamin Van Roy
In continual learning, plasticity refers to the ability of an agent to quickly adapt to new information. Neural networks are known to lose plasticity when processing non-stationary…
Continual Learning as Computationally Constrained Reinforcement Learning
Saurabh Kumar, Henrik Marklund, Ashish Rao +4
An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities…
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Ramki Gummadi, Saurabh Kumar, Junfeng Wen +1
Approaches to policy optimization have been motivated from diverse principles, based on how the parametric model is interpreted (e.g. value versus policy representation) or how the…
Characterizing the Gap Between Actor-Critic and Policy Gradient
Junfeng Wen, Saurabh Kumar, Ramki Gummadi +1
Actor-critic (AC) methods are ubiquitous in reinforcement learning. Although it is understood that AC methods are closely related to policy gradient (PG), their precise connection…
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
Saurabh Kumar, Aviral Kumar, Sergey Levine +1
While reinforcement learning algorithms can learn effective policies for complex tasks, these policies are often brittle to even minor task variations, especially when variations a…
Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta +3
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, da…