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

arXiv: https://arxiv.org/abs/2404.19462 · HTML: https://mlsanity.com/paper/2404.19462/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 23 of 23)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Towards Continual Reinforcement Learning: A Review and Perspectives](https://mlsanity.com/paper/2012.13490/) (2020)
- [Selective Experience Replay for Lifelong Learning](https://mlsanity.com/paper/1802.10269/) (2018)
- [Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer](https://mlsanity.com/paper/1609.07088/) (2016)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Modular Lifelong Reinforcement Learning via Neural Composition](https://mlsanity.com/paper/2207.00429/) (2022)
- [DisCoRL: Continual Reinforcement Learning via Policy Distillation](https://mlsanity.com/paper/1907.05855/) (2019)
- [Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/1903.06787/) (2019)
- [Coordinated Reinforcement Learning for Optimizing Mobile Networks](https://mlsanity.com/paper/2109.15175/) (2021)
- [Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization](https://mlsanity.com/paper/1906.06021/) (2019)
- [Off-policy Learning for Remote Electrical Tilt Optimization](https://mlsanity.com/paper/2005.10577/) (2020)
- [Utility Fair Optimisation of Antenna Tilt Angles in LTE Networks](https://mlsanity.com/paper/1310.1015/) (2013)
- [Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks](https://mlsanity.com/paper/2005.01472/) (2020)

## Built on by (most cited first, 1 of 1)

- [Causal Model-Based Reinforcement Learning for Sample-Efficient IoT Channel Access](https://mlsanity.com/paper/2511.10291/) (2025)

## 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.