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Download PDF Abstract: We consider the problem of high-cấp độ strategy selection in the adversarialsetting of real-time strategy games from a reinforcement learning perspective,where taking an action corresponds to lớn switching to the respective sầu strategy.Here, a good strategy successfully counters the opponent"s current và possiblefuture strategies which can only be estimated using partial observations. Weinvestigate whether we can utilize the full game state information duringtraining time (in the form of an auxiliary prediction task) to lớn increaseperformance. Experiments carried out within a StarCraft: Brood War bot againstsvào community bots show substantial win rate improvements over afixed-strategy baseline and encouraging results when learning with theauxiliary task.

Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
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