深度强化学习的纳什平衡的差异性游戏的学习
IEEE transactions on neural networks and learning systems
|January 23, 2024
概括
本研究介绍了两种深度强化学习算法,以在复杂的游戏中找到最佳策略. 新的分布式深度确定性交点政策梯度 (D4SPG) 方法更准确,更有效地解决了纳什平衡.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 计算科学 计算科学
背景情况:
- 纳什平衡对于在多代理系统中确定最佳策略至关重要.
- 传统的方法在微分游戏中与复杂的动态作斗争.
研究的目的:
- 开发新的深度强化学习算法,用于在微分游戏中解决纳什平衡.
- 为了提高准确性和效率在寻找最佳策略的冲突目标.
主要方法:
- 修改了分布式分布式深度决定性政策梯度 (D4PG) 成为双面对抗式学习框架.
- 开发了用于游戏的D4PG (D4P2G) 使用同时政策梯度下降 (SPGD).
- 引入了分布式深度决定性简单的政策梯度 (D4SPG) 与最小学习框架和简单的政策梯度调整.
主要成果:
- 在大多数模拟中,D4P2G和D4SPG算法都趋于纳什平衡.
- 与D4P2G相比,D4SPG表现出更高的准确性和效率,特别是在哈密尔顿游戏中.
- D4SPG有效地处理了传统方法无法处理的复杂游戏动态.
结论:
- 拟议的D4SPG算法在解决微分游戏方面取得了重大进展.
- 这种方法提高了在复杂的,对抗性的多代理系统中找到纳什平衡的能力.
- 深度强化学习为解决游戏理论和人工智能的挑战性问题提供了强大的工具.
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