Inspired by applications such as supply chain management, epidemics, and social networks, we formulate a stochastic game model that addresses three key features common across these domains: 1) network-structured player interactions, 2) pair-wise mixed cooperation and competition among players, and 3) limited global information toward individual decision-making. In combination, these features pose significant challenges for black box approaches taken by deep learning-based multi-agent reinforcement learning (MARL) algorithms and deserve more detailed analysis. We formulate a networked stochastic game with pair-wise general sum objectives and asymmetrical information structure, and empirically explore the effects of information availability on the outcomes of different MARL paradigms such as individual learning and centralized learning decentralized execution.

本文研究了基于随机博弈模型的多智能体强化学习中，网络结构化玩家相互作用，混合合作与竞争以及有限的全局信息对于个体决策造成的挑战以及信息可用性对于不同学习范式的影响。并通过实验，探索了不同 MARL 范式的结果，例如集中式学习分散式执行。

具有网络信息流的一般总和随机博弈