一个强大的中场演员-批判性强化学习对抗对代理国家的对抗性扰乱
概括
本研究介绍了多代理深度强化学习的强有力的中场行为者-关键 (RoMFAC). 在状态不确定性下,RoMFAC增强了决策,即使在对抗性干扰的情况下,也保持了性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多代理深度强化学习 (DRL) 依赖于代理观察,这可能是不可靠的.
- 中场演员-关键 (MFAC) 处理可扩展性,但易受状态干扰的影响,降低团队表现.
研究的目的:
- 开发一种强大的多剂型DRL方法,能够抵抗状态观测的不确定性.
- 在动态环境中提高DRL剂的可靠性和性能.
主要方法:
- 建议强大的MFAC (RoMFAC) 具有新的演员培训目标功能.
- 引入了一个行动损失函数,比较干净和对立状态.
- 采用重复的动作损失规范化,以提高演员的表现.
- 开发一个国家对抗性随机游戏 (SASG) 模型来分析强度.
主要成果:
- 罗姆法克 (RoMFAC) 证明了对抗对抗状态扰乱的稳定性.
- 拟议的方法在不受干扰的环境中保持竞争性性能.
- 实验结果验证了RoMFAC对状态不确定性的有效性.
结论:
- 对于面临状态观测不确定性的多代理DRL,RoMFAC提供了一个强大的解决方案.
- SASG模型为对抗性扰动的防御性提供了一个理论基础.
- RoMFAC增强了DRL在现实世界中与不完善信息的场景中的实际应用性.
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