# Coverage Analysis of Multi-Environment Q-Learning Algorithms for Wireless Network Optimization

arXiv: https://arxiv.org/abs/2408.16882 · HTML: https://mlsanity.com/paper/2408.16882/

## Key facts

- Cited by 2 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 13 of its 25 references that are in the map.
- First posted on arXiv 29 Aug 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, 13 of 13)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Is Q-learning Provably Efficient?](https://mlsanity.com/paper/1807.03765/) (2018)
- [Randomized Ensembled Double Q-Learning: Learning Fast Without a Model](https://mlsanity.com/paper/2101.05982/) (2021)
- [Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage](https://mlsanity.com/paper/2107.06226/) (2021)
- [Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient](https://mlsanity.com/paper/2210.06718/) (2022)
- [Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning](https://mlsanity.com/paper/1611.01929/) (2016)
- [Online Robust Reinforcement Learning with Model Uncertainty](https://mlsanity.com/paper/2109.14523/) (2021)
- [The Role of Coverage in Online Reinforcement Learning](https://mlsanity.com/paper/2210.04157/) (2022)
- [Ensemble Bootstrapping for Q-Learning](https://mlsanity.com/paper/2103.00445/) (2021)
- [Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy Optimization](https://mlsanity.com/paper/2402.05476/) (2024)
- [What can online reinforcement learning with function approximation benefit from general coverage conditions?](https://mlsanity.com/paper/2304.12886/) (2023)
- [Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks](https://mlsanity.com/paper/2402.08022/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Multi-Agent Multi-Environment Mixed Q-Learning for Partially Decentralized Wireless Network Optimization](https://mlsanity.com/paper/2409.16450/) (2024)
- [Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networks](https://mlsanity.com/paper/2503.05970/) (2025)

## Abstract (arXiv)

Q-learning is widely used to optimize wireless networks with unknown system dynamics. Recent advancements include ensemble multi-environment hybrid Q-learning algorithms, which utilize multiple Q-learning algorithms across structurally related but distinct Markovian environments and outperform existing Q-learning algorithms in terms of accuracy and complexity in large-scale wireless networks. We herein conduct a comprehensive coverage analysis to ensure optimal data coverage conditions for these algorithms. Initially, we establish upper bounds on the expectation and variance of different coverage coefficients. Leveraging these bounds, we present an algorithm for efficient initialization of these algorithms. We test our algorithm on two distinct real-world wireless networks. Numerical simulations show that our algorithm can achieve %50 less policy error and %40 less runtime complexity than state-of-the-art reinforcement learning algorithms. Furthermore, our algorithm exhibits robustness to changes in network settings and parameters. We also numerically validate our theoretical results.