摘要翻译：
在解决Agent数量呈指数级变化的挑战时，我们采用了一种基于聚类的表示方法来近似地解决多个参与者的非对称博弈。集群将对游戏有类似“战略观点”的代理人聚集在一起。我们从由策略剖面和收益组成的数据中学习聚类近似，这些数据可以从游戏观察或访问模拟器中获得。使用我们的聚类，我们构造了一个简化的“双胞胎”游戏，其中每个聚类与简化游戏的两个玩家相关联。这使得我们的表示能够独立响应，因为我们将每个个体agent的利益与其集群的策略相一致。我们的方法为智能体提供了比无模型方法和以前的基于聚类的方法更高的平均收益和更低的遗憾，并且只需要很少的观察就可以成功地学习。“双胞胎”方法被证明是提供这些低遗憾近似的一个重要组成部分。
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英文标题：
《Learning and Solving Many-Player Games through a Cluster-Based
  Representation》
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作者：
Sevan G. Ficici, David C. Parkes, Avi Pfeffer
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  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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PDF链接：
https://arxiv.org/pdf/1206.3253