24 citations · 104 across the 18 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…
STARC: A General Framework For Quantifying Differences Between Reward Functions
Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani +3
In order to solve a task using reinforcement learning, it is necessary to first formalise the goal of that task as a reward function. However, for many real-world tasks, it is very…