2018/10/09 by Piotr Gawłowicz, Gawłowicz, Piotr, Anatolij Zubow +1 · 1 citation
Computer Science · Engineering · #Advanced Optical Network Technologies #FOS: Computer and information sciences #Network Traffic and Congestion Control #Networking and Internet Architecture (cs.NI) #Software-Defined Networks and 5G #cs.NI
paper · pdf · doi:10.48550/arxiv.1810.03943
openalex publication_date 2018/10/09 · arxiv created 2018/10/10 · arxiv updated 2018/10/11 · openalex created_date 2018/10/26 · openalex updated_date 2026/07/28
OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes a large number of well-known problems that expose a common interface allowing to directly compare the performance results of different RL algorithms. Since many years, the ns-3 network simulation tool is the de-facto standard for academic and industry research into networking protocols and communications technology. Numerous scientific papers were written reporting results obtained using ns-3, and hundreds of models and modules were written and contributed to the ns-3 code base. Today as a major trend in network research we see the use of machine learning tools like RL. What is missing is the integration of a RL framework like OpenAI Gym into the network simulator ns-3. This paper presents the ns3-gym framework. First, we discuss design decisions that went into the software. Second, two illustrative examples implemented using ns3-gym are presented. Our software package is provided to the community as open source under a GPL license and hence can be easily extended.