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2023 ICML ICML 2023

Settling the Reward Hypothesis

Abstract

The reward hypothesis posits that, "all of what we mean by goals and purposes can be well thought of as maximization of the expected value of the cumulative sum of a received scalar signal (reward)." We aim to fully settle this hypothesis. This will not conclude with a simple affirmation or refutation, but rather specify completely the implicit requirements on goals and purposes under which the hypothesis holds.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Michael Bowling , John D Martin , David Abel , Will Dabney

Topics

Machine Learning > Optimization & Theory > Optimization Machine Learning > Optimization & Theory > Theory

Keywords

reinforcement learning cumulative reward expected value goal specification reward hypothesis
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