Home › Datasets › task › Continuous Control

Continuous Control datasets

archive 2025-07-28

10 datasets carry the task tag "Continuous Control" (the task itself: Continuous Control), ordered by the archive's paper count. Page 1 of 1: 10 shown of 10. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Continuous Control datasets 1–10 of 10

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.
1,305 papers · 3 benchmarks
D4RL is a collection of environments for offline reinforcement learning.
538 papers · 2 benchmarks
The DeepMind Control Suite (DMCS) is a set of simulated continuous control environments with a standardized structure and interpretable rewards.
364 papers · 3 benchmarks
Omniverse Isaac Gym is a GPU-based physics simulation platform developed by NVIDIA.
240 papers · 2 benchmarks
LANI is a 3D navigation environment and corpus, where an agent navigates between landmarks.
12 papers · 0 benchmarks
MO-Gymnasium is an open source Python library for developing and comparing multi-objective reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set…
8 papers · 0 benchmarks
MoCapAct (Motion Capture with Actions)
The MoCapAct dataset contains training data and models for humanoid locomotion research.
4 papers · 0 benchmarks
PyBullet is an easy to use Python module for physics simulation, robotics and deep reinforcement learning based on the Bullet Physics SDK.
3 papers · 4 benchmarks
RLU (RL Unplugged)
RL Unplugged is suite of benchmarks for offline reinforcement learning.
2 papers · 0 benchmarks
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…
1 paper · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.