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学习和解决许多玩家游戏—通过一个集群—基于代表性
Learning and Solving Many-Player Games through a Cluster-Based Representation |
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| 课程网址: |
http://videolectures.net/uai08_ficici_lsmpg/
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| 主讲教师: |
Ficici Sevan G
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| 开课单位: |
哈佛大学
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| 开课时间: |
2008-07-30
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| 课程语种: |
英语
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| 中文简介: |
在解决代理数量指数缩放的挑战时，我们采用基于集群的表示法来近似求解非常多参与者的非对称博弈。集群将具有类似“战略观点”的代理组织在一起。我们从包含策略配置文件和收益的数据中学习集群近似，这些数据可以从对游戏的观察或对模拟器的访问中获得。使用我们的集群，我们构建一个简化的“双胞胎”游戏，其中每个集群与简化游戏的两个玩家关联。这使得我们的代理能够单独响应，因为我们将每个代理的利益与其集群的策略相一致。我们的方法为代理提供了比无模型方法和以前基于集群的方法更高的回报和更低的遗憾，并且只需要很少的观察就可以成功学习。“双胞胎”方法被证明是提供这些低遗憾近似值的重要组成部分。
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| 课程简介: |
In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. A cluster groups together agents with a similar “strategic view” of the game. We learn the clustered approximation from data consisting of strategy profiles and payoffs, which may be obtained from observations of play or access to a simulator. Using our clustering we construct a reduced “twins” game in which each cluster is associated with two players of the reduced game. This allows our representation to be individually responsive because we align the interests of every individual agent with the strategy of its cluster. Our approach provides agents with higher payoffs and lower regret on average than model-free methods as well as previous cluster-based methods, and requires only few observations for learning to be successful. The “twins” approach is shown to be an important component of providing these low regret approximations.
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| 关 键 词: |
指数缩放代理; 战略视图; 访问模拟器
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-11-11：lxf
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| 阅读次数: |
24
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