7 papers · 1 filter
An Information-Theoretic Definition for Open-Ended Learning
Wanqiao Xu, Yifan Zhu, Benjamin Van Roy
A growing body of work points to the great promise of AI systems that can continually expand their capabilities as they operate in an open-ended environment. But yet there is no co…
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
Jonathan Colaço Carr, Jonathan Colaço Carr, Prakash Panangaden +2
Reinforcement learning with scalar rewards is widely used for aligning machine-learning systems with user preferences. But, pairwise preferences are often more natural for users to…
Efficient Exploration at Scale
Seyed Mohammad Asghari, Chris Chute, Vikranth Dwaracherla +5
We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates rew…
Capacity-Constrained Continual Learning
Zheng Wen, Doina Precup, Benjamin Van Roy +1
Any agents we can possibly build are subject to capacity constraints, as memory and compute resources are inherently finite. However, comparatively little attention has been dedica…
Satisficing Exploration for Deep Reinforcement Learning
Dilip Arumugam, Saurabh Kumar, Ramki Gummadi +1
A default assumption in the design of reinforcement-learning algorithms is that a decision-making agent always explores to learn optimal behavior. In sufficiently complex environme…
Exploration Unbound
Dilip Arumugam, Wanqiao Xu, Benjamin Van Roy
A sequential decision-making agent balances between exploring to gain new knowledge about an environment and exploiting current knowledge to maximize immediate reward. For environm…