  No-regret learning has a long history of being closely connected to game
theory. Recent works have devised uncoupled no-regret learning dynamics that,
when adopted by all the players in normal-form games, converge to various
equilibrium solutions at a near-optimal rate of $\widetilde{O}(T^{-1})$, a
significant improvement over the $O(1/\sqrt{T})$ rate of classic no-regret
learners. However, analogous convergence results are scarce in Markov games, a
more generic setting that lays the foundation for multi-agent reinforcement
learning. In this work, we close this gap by showing that the
optimistic-follow-the-regularized-leader (OFTRL) algorithm, together with
appropriate value update procedures, can find
$\widetilde{O}(T^{-1})$-approximate (coarse) correlated equilibria in
full-information general-sum Markov games within $T$ iterations. Numerical
results are also included to corroborate our theoretical findings.
