{"url":"/dataset/rllab-framework","name":"RLLab Framework","full_name":null,"description_markdown":"A benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. \r\n\r\nSource: [Benchmarking Deep Reinforcement Learning for Continuous Control](/paper/benchmarking-deep-reinforcement-learning-for)","description_withheld":null,"homepage":"https://github.com/rllab/rllab","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-deep-reinforcement-learning-for","title":"Benchmarking Deep Reinforcement Learning for Continuous Control","first_author":"Yan Duan","url":null},"license":null,"modalities":[],"tasks":[{"name":"Continuous Control","url":"/task/continuous-control","datasets_with_task":"/datasets/task/continuous-control"},{"name":"Imitation Learning","url":"/task/imitation-learning","datasets_with_task":"/datasets/task/imitation-learning"},{"name":"Policy Gradient Methods","url":"/task/policy-gradient-methods","datasets_with_task":"/datasets/task/policy-gradient-methods"}],"languages":[],"variants":["RLLab Framework"],"data_loaders":[{"repo":"https://github.com/rllab/rllab","url":"https://github.com/rllab/rllab","frameworks":["tf"]}],"num_papers_in_archive":1,"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":"2026-09-24T18:15:14+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."}