24 citations · 104 across the 18 of their papers we have counts for
4 papers · 1 filter
Lexicographic Multi-Objective Reinforcement Learning
Joar Skalse, Lewis Hammond, Charlie Griffin +1
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where t…
Misspecification in Inverse Reinforcement Learning
Joar Skalse, Alessandro Abate
The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function from a policy . To do this, we need a model of how relates to . In the current literatur…
Defining and Characterizing Reward Hacking
Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov +1
We provide the first formal definition of reward hacking, a phenomenon where optimizing an imperfect proxy reward function leads to poor performance according to the true reward fu…
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning
Joar Skalse, Matthew Farrugia-Roberts, Stuart Russell +2
It is often very challenging to manually design reward functions for complex, real-world tasks. To solve this, one can instead use reward learning to infer a reward function from d…