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Rock, Paper, StarCraft: Strategy Selection in Real-Time Strategy Games

2021; Volume: 12; Issue: 1 Linguagem: Inglês

10.1609/aiide.v12i1.12857

2334-0924

Anderson Rocha Tavares, Héctor Azpúrua, Amanda Leão dos Santos, Luiz Chaimowicz,

Reinforcement Learning in Robotics

The correct choice of strategy is crucial for a successful real-time strategy (RTS) game player. Generally speaking, a strategy determines the sequence of actions the player will take in order to defeat his/her opponents. In this paper we present a systematic study of strategy selection in the popular RTS game StarCraft. We treat the choice of strategy as a game itself and test several strategy selection techniques, including Nash Equilibrium and safe opponent exploitation. We adopt a subset of AIIDE 2015 StarCraft AI tournament bots as the available strategies and our results suggest that it is useful to deviate from Nash Equilibrium to exploit sub-optimal opponents on strategy selection, confirming insights from computer rock-paper-scissors tournaments.