Policy Optimization finds Nash Equilibrium in Regularized General-Sum LQ Games

no code implementations • 25 Mar 2024 • Muhammad Aneeq uz Zaman, Shubham Aggarwal, Melih Bastopcu, Tamer Başar

In this paper, we investigate the impact of introducing relative entropy regularization on the Nash Equilibria (NE) of General-Sum $N$-agent games, revealing the fact that the NE of such games conform to linear Gaussian policies.

Reinforcement Learning (RL)

Large Population Games on Constrained Unreliable Networks

no code implementations • 16 Mar 2023 • Shubham Aggarwal, Muhammad Aneeq uz Zaman, Melih Bastopcu, Tamer Başar

A Base station (BS) actively schedules agent communications over the network by minimizing a weighted Age of Information (WAoI) based cost function under a capacity limit $\mathcal{C} < N$ on the number of transmission attempts at each instant.

Scheduling

Weighted Age of Information based Scheduling for Large Population Games on Networks

no code implementations • 26 Sep 2022 • Shubham Aggarwal, Muhammad Aneeq uz Zaman, Melih Bastopcu, Tamer Başar

Due to a hard bandwidth constraint on the number of transmissions through the network, at most $R_d < N$ agents can concurrently access their state information through the network.

Scheduling

Linear Quadratic Mean-Field Games with Communication Constraints

no code implementations • 11 Mar 2022 • Shubham Aggarwal, Muhammad Aneeq uz Zaman, Tamer Başar

Since the complexity of solving the game increases with the number of agents, we use the Mean-Field Game paradigm to solve it.

Scheduling