  Deep model-based Reinforcement Learning (RL) has the potential to
substantially improve the sample-efficiency of deep RL. While various
challenges have long held it back, a number of papers have recently come out
reporting success with deep model-based methods. This is a great development,
but the lack of a consistent metric to evaluate such methods makes it difficult
to compare various approaches. For example, the common single-task
sample-efficiency metric conflates improvements due to model-based learning
with various other aspects, such as representation learning, making it
difficult to assess true progress on model-based RL. To address this, we
introduce an experimental setup to evaluate model-based behavior of RL methods,
inspired by work from neuroscience on detecting model-based behavior in humans
and animals. Our metric based on this setup, the Local Change Adaptation (LoCA)
regret, measures how quickly an RL method adapts to a local change in the
environment. Our metric can identify model-based behavior, even if the method
uses a poor representation and provides insight in how close a method's
behavior is from optimal model-based behavior. We use our setup to evaluate the
model-based behavior of MuZero on a variation of the classic Mountain Car task.
