Real Time Strategy

Real-time strategy (RTS) game AI research focuses on developing agents capable of complex strategic and tactical decision-making in dynamic, multi-agent environments. Current research emphasizes the application of deep reinforcement learning (DRL), large language models (LLMs), and hybrid approaches combining both, often leveraging techniques like transfer learning, imitation learning, and centralized control architectures to improve training efficiency and agent performance. These advancements are pushing the boundaries of AI capabilities in complex decision-making scenarios, with implications for both game development and broader applications in areas like autonomous cyber defense and multi-agent systems.