Papers › NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in...

NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation

30 Oct 2024arXiv:2411.04138archive 2025-07-28

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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Buffer hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository ran Apache-2.0 (permissive) · 5364f4044a619e47 · report
MLP hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository ran fingerprinted Apache-2.0 (permissive) · d7ae5ebb17778665 · report
Sherman_Morrison_inverse hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository ran · honoured contract Apache-2.0 (permissive) · 38e93fb6353025b8 · report
get_num_parameters_from_matrix hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository ran · honoured contract fingerprinted Apache-2.0 (permissive) · 3e80cb315de8ba69 · report
safe_concat hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · 835cbe687de8fb40 · report
safe_stack hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository ran · honoured contract fingerprinted Apache-2.0 (permissive) · 26f75cae6942c75c · report
CombinedBuffer hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository unverified Apache-2.0 (permissive) · 9f18e9c6c8c3eb47 · report
OfflineEnv hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository unverified Apache-2.0 (permissive) · b1d7b6c14279e8ed · report
PessimisticTD3 hmomin/networkgym/NetworkAgent/ptd3/agent.py official repository unverified Apache-2.0 (permissive) · 3a9e7d766c9d5bef · report
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compute_mean_std hmomin/CORL-compare/algorithms/finetune/iql.py found in paper text by Syntology ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 639b4986c270df90 · report
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D4RLManagementOffline RL

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AdamClipped Double Q-learningDense ConnectionsExperience ReplayReLUTD3Target Policy Smoothing

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