18 citations · 22 across the 4 of their papers we have counts for
3 papers · 1 filter
Goodhart's Law in Reinforcement Learning
Jacek Karwowski, Oliver Hayman, Xingjian Bai +3
Implementing a reward function that perfectly captures a complex task in the real world is impractical. As a result, it is often appropriate to think of the reward function as a pr…
On The Expressivity of Objective-Specification Formalisms in Reinforcement Learning
Rohan Subramani, Marcus Williams, Max Heitmann +3
Most algorithms in reinforcement learning (RL) require that the objective is formalised with a Markovian reward function. However, it is well-known that certain tasks cannot be exp…
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…