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Deep Reinforcement Learning combined with Fictitious Play shows impressiveresults on many benchmark games, most of which are, however, single-stage. Incontrast, real-world decision making problems may consist of multiple stages,where the observation spaces and the action spaces can be completely differentacross stages. We study a two-stage strategy card game Legends of Code andMagic and propose an end-to-end policy to address the difficulties that arisein multi-stage game. We also propose an optimistic smooth fictitious playalgorithm to find the Nash Equilibrium for the two-player game. Our approachwins double championships of COG2022 competition. Extensive studies verify andshow the advancement of our approach.

Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play