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In this paper, we describe the design of reinforcement-learning-based domination team (RL-DOT), a nonplayer character (NPC) team for playing Unreal Tournament (UT) Domination games. In RL-DOT, there is a commander NPC and several soldier NPCs. The running process of RL-DOT consists of several decision cycles. In each decision cycle, the commander NPC makes a decision of troop distribution and, according to that decision, sends action orders to other soldier NPCs. Each soldier NPC tries to accomplish its task in a goal-directed way, i.e., decomposing the final ultimate task (attacking or defending a domination point) into basic actions (such as running and shooting) that are directly supported by UT application programming interfaces (APIs). We use a Q-learning-style algorithm to learn the optimal decision-making policy. We carefully choose some opponent policies for our illustrative experiments. In these experiments, RL-DOT shows a distinct learning characteristic, which illustrates its efficiency in playing UT Domination games.