Reward is enough — when can we 'reinforce' the learning?

Wed, 13 Aug 2025 11:07:46 -0700

This blog post summarizes the papers Settling the Reward Hypothesis¹ and Utility Theory for Sequential Decision Making ².

The reward hypothesis is at the core of Reinforcement Learning (RL) in that whether sequential action problems can be posed as some sort of a reward maximization. One of the founders of modern RL, Richard S. Sutton, puts it as³:

“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).”

Rl on Omanshu Thapliyal

Reward is enough — when can we 'reinforce' the learning?