选择你的战斗：交互图作为战略多样性的人口级目标
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
摘要：
策略多样性在游戏中通常是必不可少的：例如，在多人游戏中，根据不同的策略对玩家进行评估将产生对其表现的更准确的估计。此外，在具有非传递性的博弈中，多样性允许参与者覆盖几个获胜策略。然而，尽管战略多样性的重要性，培训代理人表现出不同的行为仍然是一个挑战。在本文中，我们研究如何构建不同的人口代理人仔细构造如何在人口中的个人相互作用。我们的方法是基于交互图，它控制训练过程中代理之间的信息流，并可以鼓励代理专注于不同的策略，从而提高整体性能。我们提供了证据证明多样性在多智能体训练中的重要性，并分析了在一系列游戏中应用不同交互图对训练轨迹、多样性和种群性能的影响。这是在AAMAS上发表的长摘要的扩展版本。
英文摘要：
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of its performance. Furthermore, in games with non-transitivities diversity allows a player to cover several winning strategies. However, despite the significance of strategic diversity, training agents that exhibit diverse behaviour remains a challenge. In this paper we study how to construct diverse populations of agents by carefully structuring how individuals within a population interact. Our approach is based on interaction graphs, which control the flow of information between agents during training and can encourage agents to specialise on different strategies, leading to improved overall performance. We provide evidence for the importance of diversity in multi-agent training and analyse the effect of applying different interaction graphs on the training trajectories, diversity and performance of populations in a range of games. This is an extended version of the long abstract published at AAMAS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
