# Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks

arXiv: https://arxiv.org/abs/2009.06681 · HTML: https://mlsanity.com/paper/2009.06681/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks](https://mlsanity.com/paper/2112.03465/) (2021)
- [MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio](https://mlsanity.com/paper/2204.04507/) (2022)
- [Average Reward Reinforcement Learning for Wireless Radio Resource Management](https://mlsanity.com/paper/2501.06700/) (2025)
- [Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation](https://mlsanity.com/paper/2302.02250/) (2023)
- [Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2407.02342/) (2024)
- [Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management](https://mlsanity.com/paper/2608.28878/) (2026)

## Abstract (arXiv)

Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization for solving the transmit power control problem in wireless networks. The multi-agent deep reinforcement learning approach considers each transmitter as an individual learning agent that determines its transmit power level by observing the local wireless environment. Following a certain policy, these agents learn to collaboratively maximize a global objective, e.g., a sum-rate utility function. This multi-agent scheme is easily scalable and practically applicable to large-scale cellular networks. In this work, we present a distributively executed continuous power control algorithm with the help of deep actor-critic learning, and more specifically, by adapting deep deterministic policy gradient. Furthermore, we integrate the proposed power control algorithm to a time-slotted system where devices are mobile and channel conditions change rapidly. We demonstrate the functionality of the proposed algorithm using simulation results.