# Dynamic Potential Games in Communications: Fundamentals and Applications

arXiv: https://arxiv.org/abs/1509.01313 · HTML: https://mlsanity.com/paper/1509.01313/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2015.

## Built on by (most cited first, 8 of 8)

- [Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence](https://mlsanity.com/paper/2202.04129/) (2022)
- [Gradient play in stochastic games: stationary points, convergence, and sample complexity](https://mlsanity.com/paper/2106.00198/) (2021)
- [Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems](https://mlsanity.com/paper/2406.10015/) (2024)
- [Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games](https://mlsanity.com/paper/2410.22912/) (2024)
- [Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems](https://mlsanity.com/paper/2408.06397/) (2024)
- [Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems](https://mlsanity.com/paper/2408.05992/) (2024)
- [Almost Sure Convergence of Networked Policy Gradient over Time-Varying Networks in Markov Potential Games](https://mlsanity.com/paper/2410.20075/) (2024)
- [Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory](https://mlsanity.com/paper/2506.13164/) (2025)

## Abstract (arXiv)

In a noncooperative dynamic game, multiple agents operating in a changing environment aim to optimize their utilities over an infinite time horizon. Time-varying environments allow to model more realistic scenarios (e.g., mobile devices equipped with batteries, wireless communications over a fading channel, etc.). However, solving a dynamic game is a difficult task that requires dealing with multiple coupled optimal control problems. We focus our analysis on a class of problems, named textitdynamic potential games, whose solution can be found through a single multivariate optimal control problem. Our analysis generalizes previous studies by considering that the set of environment's states and the set of players' actions are constrained, as it is required by most of the applications. And the theoretical results are the natural extension of the analysis for static potential games. We apply the analysis and provide numerical methods to solve four key example problems, with different features each: energy demand control in a smart-grid network, network flow optimization in which the relays have bounded link capacity and limited battery life, uplink multiple access communication with users that have to optimize the use of their batteries, and two optimal scheduling games with nonstationary channels.