用强化学习与循环网络玩复杂军事推演，能自适应不同地形和战术。
Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks
- 结合AlphaZero与改进的循环神经网络，设计新状态与动作表示
- 少量训练即在典型场景中表现良好，具备跨地形泛化能力
- 适合研究复杂对抗策略的学者与游戏人工智能开发者
Hex和反制战棋是模拟真实军事冲突的对抗性双人仿真，需要复杂的策略决策。与经典棋类不同，这些游戏具有复杂的地形/单位交互、单位堆叠、大小不一的大地图，以及涉及数百单位的同步移动与战斗决策。本文提出一种新系统，融合前沿循环神经网络与可靠现代强化学习算法AlphaZero，采用基于现有研究改进的神经网络架构，并针对此类游戏环境设计创新的状态与动作表示。经过少量训练，该系统在典型场景中已展现良好表现，具备跨不同地形与战术情境的泛化能力。此外，我们探索了系统在更大地图规模下的可扩展性。所开发系统已开源，便于在这一挑战性领域持续研究与探索。
原文摘要 · Abstract (English)
Hex and Counter Wargames are adversarial two-player simulations of real military conflicts requiring complex strategic decision-making. Unlike classical board games, these games feature intricate terrain/unit interactions, unit stacking, large maps of varying sizes, and simultaneous move and combat decisions involving hundreds of units. This paper introduces a novel system designed to address the strategic complexity of Hex and Counter Wargames by integrating cutting-edge advancements in Recurrent Neural Networks with AlphaZero, a reliable modern Reinforcement Learning algorithm. The system utilizes a new Neural Network architecture developed from existing research, incorporating innovative state and action representations tailored to these specific game environments. With minimal training, our solution has shown promising results in typical scenarios, demonstrating the ability to generalize across different terrain and tactical situations. Additionally, we explore the system's potential to scale to larger map sizes. The developed system is openly accessible, facilitating continued research and exploration within this challenging domain.
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