63 citations · 86 across the 4 of their papers we have counts for
3 papers · 1 filter
Avoiding Tampering Incentives in Deep RL via Decoupled Approval
Jonathan Uesato, Ramana Kumar, Victoria Krakovna +3
How can we design agents that pursue a given objective when all feedback mechanisms are influenceable by the agent? Standard RL algorithms assume a secure reward function, and can…
REALab: An Embedded Perspective on Tampering
Ramana Kumar, Jonathan Uesato, Richard Ngo +3
This paper describes REALab, a platform for embedded agency research in reinforcement learning (RL). REALab is designed to model the structure of tampering problems that may arise…
Avoiding Side Effects By Considering Future Tasks
Victoria Krakovna, Laurent Orseau, Richard Ngo +2
Designing reward functions is difficult: the designer has to specify what to do (what it means to complete the task) as well as what not to do (side effects that should be avoided…