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一种多相半导体训练方法,用于使用多代理深度强化学习进行群体对抗
He Cai1, Yaoguo Luo1, Huanli Gao1
1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Computational intelligence and neuroscience
|July 17, 2023
概括
本研究介绍了一种多相半静态训练方法,用于群体对抗中的多代理深度强化学习 (MDRL). 这种方法提高了培训效率,使较弱的代理商能够更有效地从更强的代理商那里学习.
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:
- 群体对抗需要有效的多代理训练策略.
- 传统的单阶段培训对于复杂的场景可能是低效的.
- 强化学习为代理人培训提供了一个框架.
研究的目的:
- 提出和评估一种新的多相半常规训练方法,用于群体对抗.
- 提高竞争环境中代理人的培训效率和学习成果.
- 为应对在传统培训范式中无法学习的弱势代理人的挑战.
主要方法:
- 使用Unity平台开发一个3V3坦克战斗游戏模拟器.
- 从ML-Agent工具包中实现多代理近接政策优化与课程调整 (MA-POCA) 算法.
- 应用一个多阶段的学习策略,逐步提高强势代理的性能水平.
- 纳入半静态学习,强势代理人暂停对弱势对手的学习.
主要成果:
- 拟议的多相半静态培训方法与单相培训方法相比,显著提高了培训效率.
- 实验结果表明,较弱的代理商的学习成果有所改善.
- 该方法减少了有效的代理培训所需的计算成本和时间.
结论:
- 多相半静态训练方法是多代理深度强化学习中群体对抗的有效方法.
- 这一策略有助于有效地将知识从强者转移到弱者.
- 这些发现为在竞争环境中开发更有能力和更适应的人工智能代理提供了宝贵的见解.
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