Three Dogmas of Reinforcement Learning (Abel et al., 2024)

Watch David Abel present “Three Dogmas of RL”, joint work with Mark Ho and Anna Harutyunyan.

He begins by arguing that RL still lacks a first-principles definition of an agent, and then lays out three “dogmas” in modern RL:

1. We model environments rigorously, but leave agents as afterthoughts
2. We treat learning as “finding a solution” rather than continual adaptation
3. The “reward hypothesis” has implicit conditions most people never examine

Read the summary post here: https://sensorimotorai.github.io/2026/03/05/threedogmasrl/

I like this work, because it tries to take vague concepts like the reward hypothesis, and pin down their exact mathematical commitments. One of the takeaways is that representing goals with a single scalar reward requires fairly restrictive axioms, which people often violate in practice.

Curious what people here think.