  We present a reduction from reinforcement learning (RL) to no-regret online
learning based on the saddle-point formulation of RL, by which "any" online
algorithm with sublinear regret can generate policies with provable performance
guarantees. This new perspective decouples the RL problem into two parts:
regret minimization and function approximation. The first part admits a
standard online-learning analysis, and the second part can be quantified
independently of the learning algorithm. Therefore, the proposed reduction can
be used as a tool to systematically design new RL algorithms. We demonstrate
this idea by devising a simple RL algorithm based on mirror descent and the
generative-model oracle. For any $\gamma$-discounted tabular RL problem, with
probability at least $1-\delta$, it learns an $\epsilon$-optimal policy using
at most
$\tilde{O}\left(\frac{|\mathcal{S}||\mathcal{A}|\log(\frac{1}{\delta})}{(1-\gamma)^4\epsilon^2}\right)$
samples. Furthermore, this algorithm admits a direct extension to linearly
parameterized function approximators for large-scale applications, with
computation and sample complexities independent of
$|\mathcal{S}|$,$|\mathcal{A}|$, though at the cost of potential approximation
bias.
