RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols

TL;DR

RayNet is a scalable simulation platform that combines OMNeT++ and Ray/RLlib to facilitate the development and evaluation of reinforcement learning-based network protocols, improving training efficiency.

Contribution

The paper introduces RayNet, a novel integrated simulation platform that streamlines RL-based network protocol development by combining network simulation with scalable RL training.

Findings

RayNet enables faster experience collection compared to ns3-gym.

A simple RL congestion control protocol demonstrated RayNet's effectiveness.

RayNet supports scalable and adaptable RL-based network research.

Abstract

Reinforcement Learning (RL) has gained significant momentum in the development of network protocols. However, RL-based protocols are still in their infancy, and substantial research is required to build deployable solutions. Developing a protocol based on RL is a complex and challenging process that involves several model design decisions and requires significant training and evaluation in real and simulated network topologies. Network simulators offer an efficient training environment for RL-based protocols, because they are deterministic and can run in parallel. In this paper, we introduce \textit{RayNet}, a scalable and adaptable simulation platform for the development of RL-based network protocols. RayNet integrates OMNeT++, a fully programmable network simulator, with Ray/RLlib, a scalable training platform for distributed RL. RayNet facilitates the methodical development of RL-based network protocols so that researchers can focus on the problem at hand and not on implementation details of the learning aspect of their research. We developed a simple RL-based congestion control approach as a proof of concept showcasing that RayNet can be a valuable platform for RL-based research in computer networks, enabling scalable training and evaluation. We compared RayNet with \textit{ns3-gym}, a platform with similar objectives to RayNet, and showed that RayNet performs better in terms of how fast agents can collect experience in RL environments.