Improving AI Performance in Complex Real-Time Scenarios with Hierarchical Reinforcement Learning and Counterfactual Regret Minimization

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Recent milestones in Game Artificial Intelligence (AI) brought forth expert solutions for playing the games of Go, Chess, Poker, Dota2, and StarCraft II. Especially the last title highlighted the applicability of AI in complex real-time scenarios, yet only through massive Neural Network (NN) architectures. Hierarchical Reinforcement Learning provides managerial structure to an agent's decision-making process, while Counterfactual Regret Minimization evaluates action-decision making by learning from hindsight. Combining both may reduce the network size, that agents require to handle environments equally difficult to StarCraft II, or even more complex ones, such as Wargames. Upon success, the methods developed in this study could reach state-of-the-art performance in Real-Time Strategy (RTS) games environments, which could not only be used to enhance the experience of players in diverse RTS games but also push forward AI employability in real-world decision-making scenarios.