mlsanityData to 14 Oct 2026

FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning Agents

arXiv 2410.21029 ↗First posted 28 Oct 2024cs.MA cs.AI cs.MMcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Multimedia streaming accounts for the majority of traffic in today's internet. Mechanisms like adaptive bitrate streaming control the bitrate of a stream based on the estimated bandwidth, ideally resulting in smooth playback and a good Quality of Experience (QoE). However, selecting the optimal bitrate is challenging under volatile network conditions. This motivated researchers to train Reinforcement Learning (RL) agents for multimedia streaming. The considered training environments are often simplified, leading to promising results with limited applicability. Additionally, the QoE fairness across multiple streams is seldom considered by recent RL approaches. With this work, we propose a novel multi-agent environment that comprises multiple challenges of fair multimedia streaming: partial observability, multiple objectives, agent heterogeneity and asynchronicity. We provide and analyze baseline approaches across five different traffic classes to gain detailed insights into the behavior of the considered agents, and show that the commonly used Proximal Policy Optimization (PPO) algorithm is outperformed by a simple greedy heuristic. Future work includes the adaptation of multi-agent RL algorithms and further expansions of the environment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 40 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

9 of 40 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  4. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  5. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  6. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 104
  7. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  8. 2023Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Explorationcited by 18
  9. 2023PPO-ABR: Proximal Policy Optimization based Deep Reinforcement Learning for Adaptive BitRate streamingcited by 1

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  1. 2025DECAF: Learning to be Fair in Multi-agent Resource Allocation
  2. 2024Streaming Deep Reinforcement Learning Finally Works
  3. 2026FairTest: Search-Based Fairness Testing for Multi-Agent Reinforcement Learning Systems
  4. 2024Fairness in Reinforcement Learning: A Survey
  5. 2026Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
  6. 2024Imitation Learning for Adaptive Video Streaming with Future Adversarial Information Bottleneck Principle
  7. 2025AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning
  8. 2024Towards Fair and Efficient Learning-based Congestion Control