353 citations · 631 across the 9 of their papers we have counts for
7 papers · 1 filter
Domain Generalization for Robust Model-Based Offline Reinforcement Learning
Alan Clark, Shoaib Ahmed Siddiqui, Robert Kirk +3
Existing offline reinforcement learning (RL) algorithms typically assume that training data is either: 1) generated by a known policy, or 2) of entirely unknown origin. We consider…
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj +2
Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or raw data, practitioners are faced with si…
Active Reinforcement Learning: Observing Rewards at a Cost
David Krueger, Jan Leike, Owain Evans +1
Active reinforcement learning (ARL) is a variant on reinforcement learning where the agent does not observe the reward unless it chooses to pay a query cost c > 0. The central ques…
Hidden Incentives for Auto-Induced Distributional Shift
David Krueger, Tegan Maharaj, Jan Leike
Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists. An exam…
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen +5
Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world. To tackle this problem, we assume that vari…
Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt +3
One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. Designing such reward functions is difficult in part bec…