Learning Constrained Nash Equilibria in Markov Potential Games

Kernekoncepter

The author proposes an independent learning algorithm for approximating constrained Nash equilibria in Markov Potential Games, addressing the challenge of decentralized coordination.

Resumé

The content discusses the development of an independent learning algorithm for Constrained Markov Potential Games (CMPGs). It introduces a policy gradient algorithm tailored for learning approximate constrained Nash equilibria without centralized coordination. The simulations illustrate convergence to constrained Nash equilibria in scenarios like pollution tax models and energy marketplaces.

Statistik

Each agent aims to maximize their own individual reward.
Prominent applications include multi-robot control and autonomous driving.
Agents may be subject to soft constraints such as latency thresholds or power constraints.
The algorithm can be implemented independently without centralized coordination.
The proposed algorithm establishes convergence guarantees towards constrained approximate Nash equilibria.

Citater

"In this work, we propose an independent policy gradient algorithm for learning approximate constrained Nash equilibria."
"Our algorithm can be implemented independently without a centralized coordination mechanism."

Vigtigste indsigter udtrukket fra

Independent Learning in Constrained Markov Potential Games

by Philip Jorda... kl. arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17885.pdf

Dybere Forespørgsler

Can decentralized algorithms achieve the same sample complexity as centralized ones

Decentralized algorithms face challenges in achieving the same sample complexity as centralized ones due to the lack of coordination and information sharing among agents. Centralized algorithms can leverage global knowledge and optimize jointly, leading to potentially faster convergence and lower sample complexity. In contrast, decentralized algorithms require each agent to learn independently without direct communication or coordination with others. This independence introduces additional complexities such as dealing with non-stationarity from individual viewpoints, making it harder to match the performance of centralized approaches in terms of sample efficiency.

How can fully independent learning dynamics be designed for constrained settings

Designing fully independent learning dynamics for constrained settings involves creating algorithms where agents operate autonomously without any form of interaction or coordination throughout the learning process. To achieve this, each agent must make decisions based solely on its local observations while adhering to shared constraints that govern their interactions within a multi-agent system. Fully independent learning dynamics aim to promote scalability, privacy protection, and reduced communication overhead by enabling agents to learn efficiently without relying on centralized control or information exchange mechanisms.

What are potential applications beyond CMPGs for learning constrained NE

Beyond Constrained Markov Potential Games (CMPGs), there are various potential applications for learning constrained Nash equilibria (NE) in multi-agent systems. One area is resource allocation scenarios like dynamic pricing models in e-commerce platforms where sellers compete under pricing constraints while maximizing profits collectively. Another application could be traffic management systems where autonomous vehicles navigate congested road networks following safety regulations and traffic rules while optimizing travel times collectively. Additionally, energy grid optimization problems involving distributed energy resources balancing supply-demand requirements under operational constraints present another promising domain for applying constrained NE-learning techniques.