This paper provides a comprehensive review of mainly GNN, DRL, and PTM methods with a focus on their potential incorporation in strategic multiagent settings. We draw interest in (i) ML methods currently utilized for uncovering unknown model structures adaptable to the task of strategic opponent modeling, and (ii) the integration of these methods with Game Theoretic concepts that avoid relying on assumptions often invalid in real-world scenarios, such as the Common Prior Assumption (CPA) and the Self-Interest Hypothesis (SIH). We analyze the ability to handle uncertainty and heterogeneity, two characteristics that are very common in real-world application cases, as well as scalability. As a potential answer to effectively modeling relationships and interactions in multiagent settings, we champion the use of GNN. Such approaches are designed to operate upon graph-structured data, and have been shown to be a very powerful tool for performing tasks such as node classification and link prediction. Next, we review the domain of RL, and in particular that of multiagent deep reinforcement learning. Single-agent deep RL has been widely used for decision making in demanding game settings. Its application in multiagent settings though is hindered due to, e.g., varying relationships between agents, and non-stationarity of the environment. We describe existing relevant game theoretic solution concepts, and consider properties such as fairness and stability. Our review comes complete with a note on the literature that utilizes probabilistic topic modeling (PTM) in domains other than that of document analysis and classification. Finally, we identify certain open challenges -- specifically, the need to (i) fit non-stationary environments, (ii) balance the degrees of stability and adaptation, (iii) tackle uncertainty and heterogeneity, (iv) guarantee scalability and solution tractability.

翻译：本文对主要图神经网络、深度强化学习及概率主题建模方法进行了全面综述，重点探讨其在策略性多智能体场景中的潜在整合应用。我们重点关注：（i）当前用于揭示未知模型结构且适用于策略性对手建模任务的机器学习方法；（ii）将这些方法与博弈论概念相结合，以规避现实场景中往往不成立的假设依赖，例如公共先验假设与自利假设。我们分析了方法处理不确定性与异质性的能力（这两种特性在现实应用案例中极为常见）以及可扩展性。作为有效建模多智能体场景中关系与交互的潜在解决方案，我们倡导使用图神经网络。此类方法专为处理图结构数据而设计，已被证明是执行节点分类和链接预测等任务的强大工具。随后，我们综述了强化学习领域，特别是多智能体深度强化学习的进展。单智能体深度强化学习已在复杂博弈场景的决策制定中得到广泛应用，但其在多智能体场景中的应用仍受限于智能体间动态变化的关系及环境的非平稳性等因素。我们阐述了现有相关的博弈论解概念，并考量了公平性与稳定性等属性。本综述还特别梳理了概率主题建模在文档分析与分类领域之外的应用文献。最后，我们指出了若干开放挑战——具体包括需要：（i）适应非平稳环境；（ii）平衡稳定性与适应性的程度；（iii）应对不确定性与异质性；（iv）保证可扩展性与解的可处理性。