对比两种强化学习算法在无人机对抗环境中的表现，发现不同场景下各有优势。
Light Aircraft Game : Basic Implementation and training results analysis
- 设计了分层控制与奖励机制的无人机对抗环境LAG。
- 无武器时HASAC更稳定，有导弹时HAPPO适应性更强。
- 适合研究多智能体协作与竞争的算法选型者参考。
本文研究了在部分可观测、合作竞争环境LAG中的多智能体强化学习（MARL）。该环境包含代理动作、分层控制及多种战斗模式（如无武器和发射导弹）下的奖励设计。评估了两种代表性算法：基于PPO的有策略分层算法HAPPO，以及基于软演员-评论家的无策略方法HASAC。通过分析训练稳定性、奖励进展和智能体间协作能力，实验结果表明，在无武器的简单协调任务中，HASAC表现良好；而在涉及导弹作战的动态复杂场景中，HAPPO展现出更强的适应性。这些发现揭示了有策略与无策略方法在多智能体设置中的权衡关系。
原文摘要 · Abstract (English)
This paper investigates multi-agent reinforcement learning (MARL) in a partially observable, cooperative-competitive combat environment known as LAG. We describe the environment's setup, including agent actions, hierarchical controls, and reward design across different combat modes such as No Weapon and ShootMissile. Two representative algorithms are evaluated: HAPPO, an on-policy hierarchical variant of PPO, and HASAC, an off-policy method based on soft actor-critic. We analyze their training stability, reward progression, and inter-agent coordination capabilities. Experimental results show that HASAC performs well in simpler coordination tasks without weapons, while HAPPO demonstrates stronger adaptability in more dynamic and expressive scenarios involving missile combat. These findings provide insights into the trade-offs between on-policy and off-policy methods in multi-agent settings.
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