  The performance of state-of-the-art offline RL methods varies widely over the
spectrum of dataset qualities, ranging from far-from-optimal random data to
close-to-optimal expert demonstrations. We re-implement these methods to test
their reproducibility, and show that when a given method outperforms the others
on one end of the spectrum, it never does on the other end. This prevents us
from naming a victor across the board. We attribute the asymmetry to the amount
of inductive bias injected into the agent to entice it to posit that the
behavior underlying the offline dataset is optimal for the task. Our
investigations confirm that careless injections of such optimality inductive
biases make dominant agents subpar as soon as the offline policy is
sub-optimal. To bridge this gap, we generalize importance-weighted regression
methods that have proved the most versatile across the spectrum of dataset
grades into a modular framework that allows for the design of methods that
align with how much we know about the dataset. This modularity enables
qualitatively different injections of optimality inductive biases. We show that
certain orchestrations strike the right balance, improving the return on one
end of the spectrum without harming it on the other end. While the formulation
of guidelines for the design of an offline method reduces to aligning the
amount of optimality bias to inject with what we know about the quality of the
data, the design of an agnostic method for which we need not know the quality
of the data beforehand is more nuanced. Only our framework allowed us to design
a method that performed well across the spectrum while remaining modular if
more information about the quality of the data ever becomes available.

Machine Learning;Artificial Intelligence