mlsanityData to 14 Oct 2026

Benchmarking Deep Reinforcement Learning for Continuous Control

arXiv 1604.06778 ↗First posted 22 Apr 2016cs.LG cs.AI cs.ROcited by 371

Not on the map. Why?

Cited by 85 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present 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. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.

Key facts. Cited by 371 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Apr 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2018: 2; 2019: 4; 2020: 7; 2021: 90; 2022: 74; 2023: 62; 2024: 55; 2025: 48; 2026: 29.

371 papers.

Sort:

By area:

  1. 2023Training Diffusion Models with Reinforcement Learningcited by 982
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  3. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 725
  4. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 590
  5. 2022Behavior Transformers: Cloning k modes with one stonecited by 304
  6. 2021Randomized Ensembled Double Q-Learning: Learning Fast Without a Modelcited by 288
  7. 2023Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardscited by 243
  8. 2021DexMV: Imitation Learning for Dexterous Manipulation from Human Videoscited by 206
  9. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 186
  10. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
  11. 2022Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Modelscited by 174
  12. 2021Benchmarking Simulation-Based Inferencecited by 162
38 more
  1. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  2. 2021URLB: Unsupervised Reinforcement Learning Benchmarkcited by 139
  3. 2022CORL: Research-oriented Deep Offline Reinforcement Learning Librarycited by 119
  4. 2023UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learningcited by 107
  5. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  6. 2021PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physicscited by 103
  7. 2021Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithmscited by 101
  8. 2018EnsembleDAgger: A Bayesian Approach to Safe Imitation Learningcited by 100
  9. 2025Flow Matching Policy Gradientscited by 99
  10. 2020State-Only Imitation Learning for Dexterous Manipulationcited by 91
  11. 2022q-Learning in Continuous Timecited by 91
  12. 2021Parametrized quantum policies for reinforcement learningcited by 90
  13. 2022From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperationcited by 82
  14. 2021A Survey of Exploration Methods in Reinforcement Learningcited by 73
  15. 2022Constrained Variational Policy Optimization for Safe Reinforcement Learningcited by 71
  16. 2022An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methodscited by 68
  17. 2022Constrained Update Projection Approach to Safe Policy Optimizationcited by 58
  18. 2023Empirical Design in Reinforcement Learningcited by 55
  19. 2021Memory-based Deep Reinforcement Learning for POMDPscited by 54
  20. 2021Fault-Tolerant Federated Reinforcement Learning with Theoretical Guaranteecited by 54
  21. 2022VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learningcited by 52
  22. 2022Learning Generalizable Dexterous Manipulation from Human Grasp Affordancecited by 51
  23. 2018Multi-Agent Imitation Learning for Driving Simulationcited by 50
  24. 2022Imitation Learning by Estimating Expertise of Demonstratorscited by 50
  25. 2021Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learningcited by 47
  26. 2022Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robotscited by 47
  27. 2019Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreementcited by 46
  28. 2022Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policiescited by 46
  29. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learningcited by 45
  30. 2021Settling the Variance of Multi-Agent Policy Gradientscited by 45
  31. 2025Meta-World+: An Improved, Standardized, RL Benchmarkcited by 43
  32. 2021Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modelingcited by 41
  33. 2021Generalized Proximal Policy Optimization with Sample Reusecited by 40
  34. 2021Hierarchical Skills for Efficient Explorationcited by 38
  35. 2021Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policiescited by 38
  36. 2022On the Robustness of Safe Reinforcement Learning under Observational Perturbationscited by 38
  37. 2024Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applicationscited by 38
  38. 2023Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oraclescited by 35

50 most cited of 371 All 371 (JSON)