Papers › NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in...
NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation
Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta, Jing Zhu, Yu-Xiang Wang
Mobile devices such as smartphones, laptops, and tablets can often connect to multiple access networks (e.g., Wi-Fi, LTE, and 5G) simultaneously. Recent advancements facilitate seamless integration of these connections below the transport layer, enhancing the experience for apps that lack inherent multi-path support. This optimization hinges on dynamically determining the traffic distribution across networks for each device, a process referred to as \textit{multi-access traffic splitting}. This paper introduces \textit{NetworkGym}, a high-fidelity network environment simulator that facilitates generating multiple network traffic flows and multi-access traffic splitting. This simulator facilitates training and evaluating different RL-based solutions for the multi-access traffic splitting problem. Our initial explorations demonstrate that the majority of existing state-of-the-art offline RL algorithms (e.g. CQL) fail to outperform certain hand-crafted heuristic policies on average. This illustrates the urgent need to evaluate offline RL algorithms against a broader range of benchmarks, rather than relying solely on popular ones such as D4RL. We also propose an extension to the TD3+BC algorithm, named Pessimistic TD3 (PTD3), and demonstrate that it outperforms many state-of-the-art offline RL algorithms. PTD3's behavioral constraint mechanism, which relies on value-function pessimism, is theoretically motivated and relatively simple to implement.
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Code
Syntology Ran 9 of 18 code samples harvested from 2 repositories linked to this paper; 9 have no recorded run. Of those that ran: 4 ran · honoured contract; 2 ran · our draft was wrong; 1 ran · fixture could not drive it; 2 ran with no contract checked.
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Code Syntology ran Syntology
18 samples harvested; 9 ran; 4 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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