  A fundamental challenge in interactive learning and decision making, ranging
from bandit problems to reinforcement learning, is to provide sample-efficient,
adaptive learning algorithms that achieve near-optimal regret. This question is
analogous to the classical problem of optimal (supervised) statistical
learning, where there are well-known complexity measures (e.g., VC dimension
and Rademacher complexity) that govern the statistical complexity of learning.
However, characterizing the statistical complexity of interactive learning is
substantially more challenging due to the adaptive nature of the problem. The
main result of this work provides a complexity measure, the Decision-Estimation
Coefficient, that is proven to be both necessary and sufficient for
sample-efficient interactive learning. In particular, we provide:
  1. a lower bound on the optimal regret for any interactive decision making
problem, establishing the Decision-Estimation Coefficient as a fundamental
limit.
  2. a unified algorithm design principle, Estimation-to-Decisions (E2D), which
transforms any algorithm for supervised estimation into an online algorithm for
decision making. E2D attains a regret bound that matches our lower bound up to
dependence on a notion of estimation performance, thereby achieving optimal
sample-efficient learning as characterized by the Decision-Estimation
Coefficient.
  Taken together, these results constitute a theory of learnability for
interactive decision making. When applied to reinforcement learning settings,
the Decision-Estimation Coefficient recovers essentially all existing hardness
results and lower bounds. More broadly, the approach can be viewed as a
decision-theoretic analogue of the classical Le Cam theory of statistical
estimation; it also unifies a number of existing approaches -- both Bayesian
and frequentist.
