摘要翻译：
本文探讨了社会学习在游戏智能体训练中的作用。研究了智能体在社会环境中的训练，而不是在自我游戏环境中的训练。使用强化学习算法的代理是在社会环境中训练的。这模仿了拼字游戏和国际象棋等棋类游戏的玩家在俱乐部中相互指导的方式。循环赛和修改后的瑞士锦标赛设置用于训练。将使用社会环境训练的智能体与自我游戏智能体进行比较，结果表明，从社会环境训练中产生了更健壮的智能体。更高的状态空间游戏可以从这样的设置中受益，因为不同的代理集将有多个策略，增加了在训练结束时获得更有经验的玩家的机会。受过社会学习训练的Agent比自我游戏Agent表现出更好的游戏体验。随着人口规模的增加，修改后的瑞士游戏风格产生了更多更好的游戏代理。
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英文标题：
《Social Learning Methods in Board Games》
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作者：
Vukosi N. Marivate and Tshilidzi Marwala
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最新提交年份：
2008
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分类信息：

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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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
  This paper discusses the effects of social learning in training of game playing agents. The training of agents in a social context instead of a self-play environment is investigated. Agents that use the reinforcement learning algorithms are trained in social settings. This mimics the way in which players of board games such as scrabble and chess mentor each other in their clubs. A Round Robin tournament and a modified Swiss tournament setting are used for the training. The agents trained using social settings are compared to self play agents and results indicate that more robust agents emerge from the social training setting. Higher state space games can benefit from such settings as diverse set of agents will have multiple strategies that increase the chances of obtaining more experienced players at the end of training. The Social Learning trained agents exhibit better playing experience than self play agents. The modified Swiss playing style spawns a larger number of better playing agents as the population size increases.
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PDF链接：
https://arxiv.org/pdf/0810.3474