  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.
