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Evaluating deep reinforcement learning for computer generated forces in ground combat simulation

Deep learning techniques are able to process and learn from data (e.g., images, video, audio) without explicit feature extraction. As a result, not only is the manual workload to build such models reduced, but the performance and accuracy of these models can often outperform those in which the preprocessing phase embeds human intuition. In the light of these advancements this study aims to examine if current, often manual, practices and techniques for modeling tactical behavior can be improved using deep reinforcement learning (DRL). We compare three state-of-the-art DRL algorithms according to their ability to control computer generated forces in simulated ground combat scenarios. The algorithms are empirically evaluated by comparing learning curves and behavioral performance using four basic maneuverability tasks. Our results show that at least one algorithm solved all tasks without hyperparameter search.