摘要翻译：
金融市场的投资者参与了许多游戏--他们必须与其他代理人互动以实现他们的目标。其中包括那些与他们在市场上的活动直接相关的因素，但我们不能忽视影响人类决策和他们作为投资者的表现的其他方面。区分所有子游戏通常超出希望和资源消耗。本文研究了投资者在不知道博弈的完整结构的情况下，如何面对多种不同的博弈，收集信息并形成决策。为此，我们将强化学习方法应用于市场信息论模型(ITMM)。根据Mengel，我们可以尝试区分一类$\gamma$游戏和可能的行动（策略）$a^{i}_{m_{i}}$对于$i-$the Agent。任何agent都将整个博弈类划分为她/他认为相似的类比子类，因此对给定的子类采取相同的策略。划分的标准是基于利润和成本分析。类比类和类比策略在学习过程中的不同阶段不断更新。这条研究线可以在不同的方向上继续下去。
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英文标题：
《Reinforcement learning in market games》
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作者：
Edward W. Piotrowski, Jan Sladkowski, Anna Szczypinska
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  Financial markets investors are involved in many games -- they must interact with other agents to achieve their goals. Among them are those directly connected with their activity on markets but one cannot neglect other aspects that influence human decisions and their performance as investors. Distinguishing all subgames is usually beyond hope and resource consuming. In this paper we study how investors facing many different games, gather information and form their decision despite being unaware of the complete structure of the game. To this end we apply reinforcement learning methods to the Information Theory Model of Markets (ITMM). Following Mengel, we can try to distinguish a class $\Gamma$ of games and possible actions (strategies) $a^{i}_{m_{i}}$ for $i-$th agent. Any agent divides the whole class of games into analogy subclasses she/he thinks are analogous and therefore adopts the same strategy for a given subclass. The criteria for partitioning are based on profit and costs analysis. The analogy classes and strategies are updated at various stages through the process of learning. This line of research can be continued in various directions.
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PDF链接：
https://arxiv.org/pdf/0710.0114