{"url":"/dataset/lemgorl","name":"LemgoRL","full_name":null,"description_markdown":"LemgoRL is an open-source benchmark tool for traffic signal control designed to train reinforcement learning agents in a highly realistic simulation scenario with the aim to reduce Sim2Real gap. In addition to the realistic simulation model, LemgoRL encompasses a traffic signal logic unit that ensures compliance with all regulatory and safety requirements. LemgoRL offers the same interface as the well-known OpenAI gym toolkit to enable easy deployment in existing research work.","description_withheld":null,"homepage":"https://github.com/rl-ina/lemgorl","introduced_date":"2021-03-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/lemgorl-an-open-source-benchmark-tool-to","title":"Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control","first_author":"Arthur Müller","url":null},"license":{"name":"GNU GPL V3.0 License","url":"https://github.com/RL-INA/LemgoRL/blob/main/LICENSE.txt"},"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[],"languages":[],"variants":["LemgoRL"],"data_loaders":[{"repo":"https://github.com/rl-ina/lemgorl","url":"https://github.com/rl-ina/lemgorl","frameworks":[]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}