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Evolving Reactive Micromanagement Controller for Real-Time Strategy Games 1

Real-Time Strategy (RTS) games are a genre of video games representing an interesting, well-defined adversarial domain for Artificial Intelligence (AI) research. One of many subproblems that RTS players need to solve is the micromanagement of individual units (simple agents carrying out player’s commands) during combat. Numerous multi-agent reactive control mechanisms have already been developed to maximize the combat efficiency of controlled units. Majority of these mechanisms make use of numeric parameters that need to be fine-tuned in order to achieve desired behavior. Due to a large number of these parameters, assigning them manually is inconvenient and training them by machine learning methods usually takes a long time (search space is too large). To reduce the search space and accelerate the training, we propose a simple reactive controller with only eight parameters. We implement it for a classic RTS game StarCraft: Brood War and train the parameters using genetic algorithms. Our experiments demonstrate an impressive combat performance after only a small number of generations. Keywords—Genetic Algorithms, Evolution, RTS, micromanagement, StarCraft.