451 citations · 1.7k across the 73 of their papers we have counts for
76 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…
On the Convergence of Bounded Agents
David Abel, André Barreto, Hado van Hasselt +3
When has an agent converged? Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence: An agent converges when its behavior or…
A Definition of Continual Reinforcement Learning
David Abel, André Barreto, Benjamin Van Roy +3
In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on…
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…
Scalable Neural Contextual Bandit for Recommender Systems
Zheqing Zhu, Benjamin Van Roy
High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neu…
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models
Wanqiao Xu, Shi Dong, Dilip Arumugam +1
A centerpiece of the ever-popular reinforcement learning from human feedback (RLHF) approach to fine-tuning autoregressive language models is the explicit training of a reward mode…