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)

Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective

no code implementations • 17 Mar 2024 • Muhammad Aneeq uz Zaman, Alec Koppel, Mathieu Laurière, Tamer Başar

This MFTG NE is then shown to be $\mathcal{O}(1/M)$-NE for the finite population game where $M$ is a lower bound on the number of agents in each team.

Problem Decomposition 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

Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path

no code implementations • 24 Aug 2022 • Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt, Tamer Başar

Given that the underlying Markov Decision Process (MDP) of the agent is communicating, we provide finite sample convergence guarantees in terms of convergence of the mean-field and control policy to the mean-field equilibrium.

reinforcement-learning Reinforcement Learning (RL)

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

Adversarial Linear-Quadratic Mean-Field Games over Multigraphs

no code implementations • 29 Sep 2021 • Muhammad Aneeq uz Zaman, Sujay Bhatt, Tamer Başar

In this paper, we propose a game between an exogenous adversary and a network of agents connected via a multigraph.

Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games

no code implementations • 9 Sep 2020 • Muhammad Aneeq uz Zaman, Kaiqing Zhang, Erik Miehling, Tamer Başar

We propose an actor-critic algorithm to iteratively compute the mean-field equilibrium (MFE) of the LQ-MFG.

Multi-agent Reinforcement Learning reinforcement-learning +1

Multi-agent Planning for thermalling gliders using multi level graph-search

no code implementations • 2 Jul 2020 • Muhammad Aneeq uz Zaman, Aamer Iqbal Bhatti

Two ways are presented of solving this problem, a brute force search approach as shown in earlier work and a Branch\&Bound type graph search.