Semantic Communication in Multi-team Dynamic Games: A Mean Field Perspective

TL;DR

This paper develops a mean-field game framework for large-scale multi-team dynamic games with semantic communication, providing approximate Nash equilibria and analyzing their properties in networked multi-agent systems.

Contribution

It introduces a novel mean-field approach to multi-team dynamic games with semantic communication, addressing the challenges of decentralized control and noncooperative interactions.

Findings

Derived approximate decentralized Nash equilibria for large multi-team systems.

Proved the $$-Nash property of the mean-field equilibrium.

Validated theoretical results through extensive numerical simulations.

Abstract

Coordinating communication and control is a key component in the stability and performance of networked multi-agent systems. While single user networked control systems have gained a lot of attention within this domain, in this work, we address the more challenging problem of large population multi-team dynamic games. In particular, each team constitutes two decision makers (namely, the sensor and the controller) who coordinate over a shared network to control a dynamically evolving state of interest under costs on both actuation and sensing/communication. Due to the shared nature of the wireless channel, the overall cost of each team depends on other teams' policies, thereby leading to a noncooperative game setup. Due to the presence of a large number of teams, we compute approximate decentralized Nash equilibrium policies for each team using the paradigm of (extended) mean-field games, which is governed by (1) the mean traffic flowing over the channel, and (2) the value of information at the sensor, which highlights the semantic nature of the ensuing communication. In the process, we compute optimal controller policies and approximately optimal sensor policies for each representative team of the mean-field system to alleviate the problem of general non-contractivity of the mean-field fixed point operator associated with the finite cardinality of the sensor action space. Consequently, we also prove the $\epsilon$--Nash property of the mean-field equilibrium solution which essentially characterizes how well the solution derived using mean-field analysis performs on the finite-team system. Finally, we provide extensive numerical simulations, which corroborate the theoretical findings and lead to additional insights on the properties of the results presented.