# Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication

arXiv: https://arxiv.org/abs/1911.03264 · HTML: https://mlsanity.com/paper/1911.03264/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

## Built on by (most cited first, 21 of 21)

- [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024)
- [Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study](https://mlsanity.com/paper/2303.17114/) (2023)
- [Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks](https://mlsanity.com/paper/2309.13223/) (2023)
- [Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2407.15784/) (2024)
- [Generative Diffusion Models for Resource Allocation in Wireless Networks](https://mlsanity.com/paper/2504.20277/) (2025)
- [Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks](https://mlsanity.com/paper/2207.00414/) (2022)
- [Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks](https://mlsanity.com/paper/2209.13532/) (2022)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Advancing RAN Slicing with Offline Reinforcement Learning](https://mlsanity.com/paper/2312.10547/) (2023)
- [Graph Signal Diffusion Models for Wireless Resource Allocation](https://mlsanity.com/paper/2604.05175/) (2026)
- [Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning](https://mlsanity.com/paper/2409.18718/) (2024)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [Towards Federated Learning-Enabled Visible Light Communication in 6G Systems](https://mlsanity.com/paper/2110.03319/) (2021)
- [Robust Deep Reinforcement Learning Scheduling via Weight Anchoring](https://mlsanity.com/paper/2304.10176/) (2023)
- [A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation](https://mlsanity.com/paper/2304.12660/) (2023)
- [Deep Reinforcement Learning-Based Precoding for Multi-RIS-Aided Multiuser Downlink Systems with Practical Phase Shift](https://mlsanity.com/paper/2509.25661/) (2025)
- [EVT-Based Generative AI for Tail-Aware Channel Estimation](https://mlsanity.com/paper/2604.25008/) (2026)
- [Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks](https://mlsanity.com/paper/2201.10032/) (2022)
- [On the Importance of Exploration for Real Life Learned Algorithms](https://mlsanity.com/paper/2304.10860/) (2023)
- [Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2307.13415/) (2023)
- [Generative AI Enabled Matching for 6G Multiple Access](https://mlsanity.com/paper/2411.04137/) (2024)

## Abstract (arXiv)

In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC). The proposed, experienced deep-RL framework can guarantee high end-to-end reliability and low end-to-end latency, under explicit data rate constraints, for each wireless without any models of or assumptions on the users' traffic. In particular, in order to enable the deep-RL framework to account for extreme network conditions and operate in highly reliable systems, a new approach based on generative adversarial networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL framework using a mix of real and synthetic data, thus creating an experienced deep-RL framework that has been exposed to a broad range of network conditions. Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints. To solve this problem using experienced deep-RL, first, the rate of each user is determined. Then, these rates are mapped to the resource block and power allocation vectors of the studied wireless system. Finally, the end-to-end reliability and latency of each user are used as feedback to the deep-RL framework. It is then shown that at the fixed-point of the deep-RL algorithm, the reliability and latency of the users are near-optimal. Moreover, for the proposed GAN approach, a theoretical limit for the generator output is analytically derived. Simulation results show how the proposed approach can achieve near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. The results also show that the proposed experienced deep-RL framework is able to remove the transient training time that makes conventional deep-RL methods unsuitable for URLLC.