18 citations · 22 across the 4 of their papers we have counts for
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cs.LG2023★ 1 cited
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…
cs.LG2023
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…