In this unprecedented era of technology-driven transformation, it becomes
more critical than ever that we aggressively invest in developing robust
artificial intelligence (AI) for wargaming in support of decision-making. By
advancing AI-enabled systems and pairing these with human judgment, we will be
able to enhance all-domain awareness, improve the speed and quality of our
decision cycles, offer recommendations for novel courses of action, and more
rapidly counter our adversary's actions. It therefore becomes imperative that
we accelerate the development of AI to help us better address the complexity of
modern challenges and dilemmas that currently requires human intelligence and,
if possible, attempt to surpass human intelligence--not to replace humans, but
to augment and better inform human decision-making at machine speed. Although
deep reinforcement learning continues to show promising results in intelligent
agent behavior development for the long-horizon, complex tasks typically found
in combat modeling and simulation, further research is needed to enable the
scaling of AI to deal with these intricate and expansive state-spaces
characteristic of wargaming for either concept development, education, or
analysis. To help address this challenge, in our research, we are developing
and implementing a hierarchical reinforcement learning framework that includes
a multi-model approach and dimension-invariant observation abstractions.

本文介绍了在技术驱动的转型时代，加强对人工智能在决策支持军事模拟中的应用的投资的重要性，通过推进人工智能系统和人类判断的结合来提高全域意识、改善决策周期的速度和质量、提供新型行动建议以及更迅速地应对对手行动；同时提出了通过深度强化学习来开发智能代理行为的前景与挑战，进一步讨论了通过多模型方法和维度不变观察抽象来开发层次强化学习框架的研究。