General sum stochastic games with networked information flows

TL;DR

This paper introduces a networked stochastic game model with mixed cooperation and competition, limited information, and pair-wise interactions, analyzing how information availability impacts multi-agent reinforcement learning outcomes.

Contribution

It formulates a novel stochastic game model capturing key features of networked interactions with asymmetrical information, and empirically studies MARL performance under different information structures.

Findings

Information availability significantly affects MARL outcomes.

Network structure influences cooperation and competition dynamics.

Different MARL paradigms respond variably to information constraints.

Abstract

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.