Three Dogmas of Reinforcement Learning
This is a foundational critique targeting the entire reinforcement learning community, aiming to shift paradigms rather than offering incremental improvements.
The paper critiques three foundational assumptions in reinforcement learning—environment spotlight, task-based learning, and the reward hypothesis—arguing they limit the field's potential as a framework for intelligent agents, and proposes discarding the first two while re-evaluating the third.
Modern reinforcement learning has been conditioned by at least three dogmas. The first is the environment spotlight, which refers to our tendency to focus on modeling environments rather than agents. The second is our treatment of learning as finding the solution to a task, rather than adaptation. The third is the reward hypothesis, which states that all goals and purposes can be well thought of as maximization of a reward signal. These three dogmas shape much of what we think of as the science of reinforcement learning. While each of the dogmas have played an important role in developing the field, it is time we bring them to the surface and reflect on whether they belong as basic ingredients of our scientific paradigm. In order to realize the potential of reinforcement learning as a canonical frame for researching intelligent agents, we suggest that it is time we shed dogmas one and two entirely, and embrace a nuanced approach to the third.