  In this paper, we present exploitability descent, a new algorithm to compute
approximate equilibria in two-player zero-sum extensive-form games with
imperfect information, by direct policy optimization against worst-case
opponents. We prove that when following this optimization, the exploitability
of a player's strategy converges asymptotically to zero, and hence when both
players employ this optimization, the joint policies converge to a Nash
equilibrium. Unlike fictitious play (XFP) and counterfactual regret
minimization (CFR), our convergence result pertains to the policies being
optimized rather than the average policies. Our experiments demonstrate
convergence rates comparable to XFP and CFR in four benchmark games in the
tabular case. Using function approximation, we find that our algorithm
outperforms the tabular version in two of the games, which, to the best of our
knowledge, is the first such result in imperfect information games among this
class of algorithms.
