mlsanityData to 7 Oct 2026

DeepMind Control Suite

arXiv 1801.00690 ↗First posted 2 Jan 2018cs.AIcited by 935

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Cited by 146 in World models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify. We include benchmarks for several learning algorithms. The Control Suite is publicly available at https://www.github.com/deepmind/dm_control . A video summary of all tasks is available at http://youtu.be/rAai4QzcYbs .

Key facts. Cited by 935 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jan 2018.

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Citing papers per year: 2018: 1; 2019: 2; 2020: 5; 2021: 116; 2022: 123; 2023: 154; 2024: 172; 2025: 175; 2026: 187.

935 papers.

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  1. 2023Mastering Diverse Domains through World Modelscited by 1,193
  2. 2022A Generalist Agentcited by 802
  3. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 781
  4. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  5. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
  6. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 469
  7. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 450
  8. 2023Learning Interactive Real-World Simulatorscited by 440
  9. 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 375
  10. 2022Temporal Difference Learning for Model Predictive Controlcited by 375
  11. 2021COMBO: Conservative Offline Model-Based Policy Optimizationcited by 365
  12. 2021Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learningcited by 359
38 more
  1. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 298
  2. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  3. 2022Masked Visual Pre-training for Motor Controlcited by 246
  4. 2026LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixelscited by 237
  5. 2021Mastering Atari Games with Limited Datacited by 235
  6. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 228
  7. 2024OGBench: Benchmarking Offline Goal-Conditioned RLcited by 216
  8. 2023Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?cited by 215
  9. 2020Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learningcited by 213
  10. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 193
  11. 2021Reinforcement Learning with Prototypical Representationscited by 179
  12. 2021Multi-Task Reinforcement Learning with Context-based Representationscited by 169
  13. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 164
  14. 2021BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environmentscited by 161
  15. 2022The Unsurprising Effectiveness of Pre-Trained Vision Models for Controlcited by 155
  16. 2022TransDreamer: Reinforcement Learning with Transformer World Modelscited by 143
  17. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 141
  18. 2024BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulationcited by 140
  19. 2021URLB: Unsupervised Reinforcement Learning Benchmarkcited by 138
  20. 2021The Benchmark Lotterycited by 133
  21. 2023FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulationcited by 132
  22. 2021Discovering and Achieving Goals via World Modelscited by 131
  23. 2023Extreme Q-Learning: MaxEnt RL without Entropycited by 113
  24. 2023Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learningcited by 111
  25. 2024Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyondcited by 111
  26. 2023Structured State Space Models for In-Context Reinforcement Learningcited by 109
  27. 2023RoboCat: A Self-Improving Generalist Agent for Robotic Manipulationcited by 108
  28. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  29. 2024Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous controlcited by 104
  30. 2021Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentationcited by 101
  31. 2024HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulationcited by 101
  32. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 99
  33. 2023Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysiscited by 99
  34. 2021The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixelscited by 97
  35. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  36. 2022SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learningcited by 96
  37. 2022Deep Hierarchical Planning from Pixelscited by 96
  38. 2022Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learningcited by 93

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