Emergent World Beliefs: Exploring Transformers in Stochastic Games

This article, sourced from ArXiv, likely presents research on how Transformer models, a type of neural network architecture, are used to understand and model the beliefs of agents within stochastic games. The focus is on how these models can learn and represent the 'world beliefs' of these agents, which is crucial for strategic decision-making in uncertain environments. The use of stochastic games suggests the research deals with scenarios where outcomes are probabilistic, adding complexity to the modeling task.