Theformer means that the algorithm will work nearly optimally in all environments in an adversarial setting, a stochastic setting, or a stochastic setting with adversarial corruptions.

Theformer means that the algorithm will work nearly optimally in all environments in an adversarial setting, a stochastic setting, or a stochastic setting with adversarial corruptions.

This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn theoptimal policy by playing againstitself without any direct supervision.

By and large, almost all methods described above concerns optimizing over a parametric space of policies with a fixed state-action space, which are called thepolicy scope.

Itconsists4-regular 10 different passingapproaches Table 1: Classification Method Mean MP-GNNs 0.000 10.000 RingGNN 0.000 10.000 3WLGNN 10.916 30.000