Designing a Reinforcement Learning-based Adaptive AI for Large-Scale Strategy Games

Charles Madeira, Vincent Corruble and Geber Ramalho

This paper investigates the challenges posed by the application of reinforcement learning to large-scale strategy games. In this context, we present steps and techniques which synthesize new ideas with state-of-the-art techniques from several areas of machine learning in a novel integrated learning approach for this kind of games. The performance of the approach is demonstrated on the task of learning valuable game strategies for a commercial wargame.


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