相关实验视频
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学习与任何混合对手对抗比赛
Max Olan Smith1, Thomas Anthony2, Michael P Wellman1
1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States.
Frontiers in artificial intelligence
|August 7, 2023
概括
这项研究介绍了Q-Mixing,这是人工智能代理的转移学习方法. Q-Mixing使代理人能够在不需要再培训的情况下适应对手的新策略组合,从而改善政策构建.
科学领域:
- 人工智能的人工智能
- 强化学习是一种强化学习.
- 游戏理论 游戏理论
背景情况:
- 将人工智能代理调整为不同的对手策略是具有挑战性的.
- 为新的对手混合物重新训练代理是计算上昂贵的.
- 转移学习为有效的适应提供了一个潜在的解决方案.
研究的目的:
- 提出和验证Q-Mixing,一种新的转移学习方法.
- 为了使代理人能够构建针对新的对手混合物的政策,而无需重新培训.
- 通过纳入对手政策分类来提高代理业绩.
主要方法:
- 在Q-Mixing中,Q-Mixing可以学习Q-value对抗纯策略对手.
- 它通过平均纯策略Q值来近似混合策略的Q值.
- 一个对手政策分类器使用观察到的数据来改进Q值混合.
主要成果:
- Q-Mixing成功地将知识传输到不同的对手策略混合中.
- 该方法在网格世界足球和社会困境游戏中得到了验证.
- 使用分类器增强Q-Mixing比直接训练提高了性能.
结论:
- 在多代理系统中,Q-Mixing提供了一种有效的转移学习方法.
- 该方法允许代理人将政策概括为看不见的对手策略分布.
- 整合对手政策分类进一步增强了适应能力.
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