# Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

arXiv: https://arxiv.org/abs/1808.00490 · HTML: https://mlsanity.com/paper/1808.00490/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2018.

## Built on by (most cited first, 30 of 70)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [State-Augmented Learnable Algorithms for Resource Management in Wireless Networks](https://mlsanity.com/paper/2207.02242/) (2022)
- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2105.01696/) (2021)
- [Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets](https://mlsanity.com/paper/2311.11423/) (2023)
- [Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.08421/) (2024)
- [Sparse Adversarial Attack in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2205.09362/) (2022)
- [Consensus Learning for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.02583/) (2022)
- [Coordinated Reinforcement Learning for Optimizing Mobile Networks](https://mlsanity.com/paper/2109.15175/) (2021)
- [Power Allocation for Wireless Federated Learning using Graph Neural Networks](https://mlsanity.com/paper/2111.07480/) (2021)
- [Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks](https://mlsanity.com/paper/2112.03465/) (2021)
- [Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks](https://mlsanity.com/paper/2201.11799/) (2022)
- [Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10968/) (2022)
- [Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.04672/) (2024)
- [An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management](https://mlsanity.com/paper/2501.12991/) (2025)
- [Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees](https://mlsanity.com/paper/2105.04077/) (2021)
- [Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels](https://mlsanity.com/paper/2110.01722/) (2021)
- [AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities](https://mlsanity.com/paper/2311.04241/) (2023)
- [Efficiently Quantifying Individual Agent Importance in Cooperative MARL](https://mlsanity.com/paper/2312.08466/) (2023)
- [Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models](https://mlsanity.com/paper/2401.07553/) (2024)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks](https://mlsanity.com/paper/2103.11284/) (2021)
- [Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning](https://mlsanity.com/paper/2208.14074/) (2022)
- [Learning to Transmit with Provable Guarantees in Wireless Federated Learning](https://mlsanity.com/paper/2304.09329/) (2023)
- [Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications](https://mlsanity.com/paper/2407.13947/) (2024)

## Abstract (arXiv)

This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in wireless networks. Existing techniques typically find near-optimal power allocations by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a distributively executed dynamic power allocation scheme is developed based on model-free deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from several neighbors and adapts its own transmit power accordingly. The objective is to maximize a weighted sum-rate utility function, which can be particularized to achieve maximum sum-rate or proportionally fair scheduling. Both random variations and delays in the CSI are inherently addressed using deep Q-learning. For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents. The proposed scheme is especially suitable for practical scenarios where the system model is inaccurate and CSI delay is non-negligible.