  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.

Machine Learning