# Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning

arXiv: https://arxiv.org/abs/2507.05785 · HTML: https://mlsanity.com/paper/2507.05785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Offline multi-agent RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Placed in

- Offline RL and imitation learning from data / [Offline multi-agent RL](https://mlsanity.com/offlinerl/offline-multi-agent-rl/)

## Builds on (most cited first, 6 of 6)

- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble](https://mlsanity.com/paper/2110.01548/) (2021)
- [A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems](https://mlsanity.com/paper/2203.01387/) (2022)
- [ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge](https://mlsanity.com/paper/2403.06324/) (2024)
- [Mowgli: Passively Learned Rate Control for Real-Time Video](https://mlsanity.com/paper/2410.03339/) (2024)
- [From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning](https://mlsanity.com/paper/2301.05541/) (2023)

## Abstract (arXiv)

Accurate bandwidth estimation (BWE) is critical for real-time communication (RTC) systems. Traditional heuristic approaches offer limited adaptability under dynamic networks, while online reinforcement learning (RL) suffers from high exploration costs and potential service disruptions. Offline RL, which leverages high-quality data collected from real-world environments, offers a promising alternative. However, challenges such as out-of-distribution (OOD) actions, policy extraction from behaviorally diverse datasets, and reliable deployment in production systems remain unsolved. We propose RBWE, a robust bandwidth estimation framework based on offline RL that integrates Q-ensemble (an ensemble of Q-functions) with a Gaussian mixture policy to mitigate OOD risks and enhance policy learning. A fallback mechanism ensures deployment stability by switching to heuristic methods under high uncertainty. Experimental results show that RBWE reduces overestimation errors by 18% and improves the 10th percentile Quality of Experience (QoE) by 18.6%, demonstrating its practical effectiveness in real-world RTC applications. The implementation is publicly available at https://github.com/jiu2021/RBWE_offline.