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ICML 2023

Settling the Reward Hypothesis

Conference Paper Accepted Paper Artificial Intelligence ยท Machine Learning

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.

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Context

Venue
International Conference on Machine Learning
Archive span
1993-2025
Indexed papers
16471
Paper id
315304339191620575
v2026.09.13