mlsanityData to 7 Oct 2026

Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation

arXiv 2404.19462 ↗First posted 30 Apr 2024cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

We present a method that addresses the pain point of long lead-time required to deploy cell-level parameter optimisation policies to new wireless network sites. Given a sequence of action spaces represented by overlapping subsets of cell-level configuration parameters provided by domain experts, we formulate throughput optimisation as Continual Reinforcement Learning of control policies. Simulation results suggest that the proposed system is able to shorten the end-to-end deployment lead-time by two-fold compared to a reinitialise-and-retrain baseline without any drop in optimisation gain.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

23 of 33 references.

References · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  3. 2016Learning without Forgettingcited by 2,465
  4. 2016Progressive Neural Networkscited by 1,332
  5. 2018Experience Replay for Continual Learningcited by 1,074
  6. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 748
  7. 2017Lifelong Learning with Dynamically Expandable Networkscited by 689
  8. 2018Progress & Compress: A scalable framework for continual learningcited by 516
  9. 2015Policy Distillationcited by 414
  10. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  11. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  12. 2020Towards Continual Reinforcement Learning: A Review and Perspectivescited by 268
11 more
  1. 2018Selective Experience Replay for Lifelong Learningcited by 239
  2. 2016Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfercited by 124
  3. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  4. 2022Modular Lifelong Reinforcement Learning via Neural Compositioncited by 39
  5. 2019DisCoRL: Continual Reinforcement Learning via Policy Distillationcited by 35
  6. 2019Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learningcited by 13
  7. 2021Coordinated Reinforcement Learning for Optimizing Mobile Networkscited by 7
  8. 2019Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimizationcited by 6
  9. 2020Off-policy Learning for Remote Electrical Tilt Optimizationcited by 6
  10. 2013Utility Fair Optimisation of Antenna Tilt Angles in LTE Networkscited by 3
  11. 2020Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networkscited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Causal Model-Based Reinforcement Learning for Sample-Efficient IoT Channel Accesscited by 0