← Search

Charlie Griffin

3 accepted papers

2024

Goodhart's Law in Reinforcement Learning

ICLR 2024poster

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 *proxy* for the true objective rather than as its definition. We study this phenomenon through the lens of *Goodhart’s law…

Cited by 13SourcePDFScholar
2024

On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning

ICLR 2024poster

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 expressed by means of an objective in the Markov rewards formalism, motivating the study of alternative objective-specifica…

Cited by 5SourcePDFScholar
2022

Lexicographic Multi-Objective Reinforcement Learning

IJCAI 2022poster

In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the…