Robustness of coevolved strategies in a real-time strategy game

This paper evaluates the performance of real-time strategy game strategies produced by coevolution. Specifically, we evaluate the robustness of coevolution solutions by having them compete against solutions produced by a genetic algorithm, three hand-tuned baselines, and a human opponent. Our earlier work has shown that genetic algorithms routinely find optimal solutions for defeating the opponents used in training. In this paper, our results show that coevolution finds strategies that defeat genetic algorithm strategies and two of our baselines, without seeing any of the baselines or genetic algorithm solutions during evaluation. A human player who competed against these strategies found that the coevolutionary strategy was the most challenging to defeat, but could be easily defeated using strategies not previously encountered by coevolution. This work informs our research on improving the robustness of real-time strategy players through coevolutionary approaches.