What it does ns3-gym is a C++ framework that wraps the ns-3 network simulator as an OpenAI Gym environment. You implement six callback functions in your ns-3 simulation—observation space, action space, reward logic, and so on—and Python RL agents can then step through live packet-level simulations as if they were playing CartPole.

The interesting bit The integration is bidirectional and real-time: the agent doesn’t train on a log file or pre-captured trace, it steers an actual running ns-3 process. The included cognitive radio example shows an agent learning to dodge a periodic interferer by sensing channel occupancy, with reward shaped purely by collision outcomes inside the simulator.

Key highlights

• Exposes any ns-3 variable or parameter to the RL agent through a generic C++ interface
• Ships with a working cognitive radio example (channel selection against periodic interference)
• Includes a TCP congestion-control hook: monitor socket state and manipulate CongestionWindow and SlowStartThreshold from Python
• Python-side ns3gym package installs via pip from the repo itself
• Published and maintained by TU Berlin networking researchers since 2019

Caveats

• RL-TCP example is explicitly “still under development”; only the interface and a TCP NewReno proof-of-concept are released
• Requires careful directory naming (contrib/opengym, not ns3-gym) and branch selection (app-ns-3.36+) during install
• gym.make('ns3-v0') is broken with newer OpenAI Gym; you must use ns3env.Ns3Env() directly

Verdict Worth a look if you’re doing networking research and need RL baselines against realistic MAC-layer or transport behavior. Skip it if you want polished, ready-to-train environments—this is academic infrastructure with rough edges still visible.