Recent successes of game AIs such as AlphaGo and AlphaStar, which beat professional human players in the games Go and StarCraft, respectively, mark the breakthroughs of intelligent decision making technique in complex games. Generally, games studied previously are mostly symmetric in game-theoretic sense due to their sports or e-sports characteristics. However, games in reality are usually asymmetric because of the position-dependent resource unbalance, and they are rarely studied. In this paper, we propose a novel asymmetric game model based on the framework of game theoretic learning. Specifically, we develop an agent training method with three steps: game model formulation, solution concept definition and game solution computation. To verify our model, a mini-Wargame is used in our experiment, where the initial number and visual scope are set to be unbalanced. Experiments show that the proposed method is better than popular self-play based methods such as naive self-play and prioritized fictitious self-play. The work provides a game-theoretic view for asymmetric games, and it may attract more interests for the rarely studied asymmetric games.