  The counterfactual regret minimization (CFR) family of algorithms consists of
iterative algorithms for imperfect-information games. In two-player zero-sum
games, the time average of the iterates converges to a Nash equilibrium. The
state-of-the-art prior variants, Discounted CFR (DCFR) and Predictive CFR$^+$
(PCFR$^+$) are the fastest known algorithms for solving two-player zero-sum
games in practice, both in the extensive-form setting and the normal-form
setting. They enhance the convergence rate compared to vanilla CFR by applying
discounted weights to early iterations in various ways, leveraging fixed
weighting schemes. We introduce Hyperparameter Schedules (HSs), which are
remarkably simple yet highly effective in expediting the rate of convergence.
HS dynamically adjusts the hyperparameter governing the discounting scheme of
CFR variants. HSs on top of DCFR or PCFR$^+$ is now the new state of the art in
solving zero-sum games and yields orders-of-magnitude speed improvements. The
new algorithms are also easy to implement because 1) they are small
modifications to the existing ones in terms of code and 2) they require no
game-specific tuning.
