mlsanityData to 14 Oct 2026

RRLS : Robust Reinforcement Learning Suite

arXiv 2406.08406 ↗First posted 12 Jun 2024cs.LGcited by 2

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

Robust reinforcement learning is the problem of learning control policies that provide optimal worst-case performance against a span of adversarial environments. It is a crucial ingredient for deploying algorithms in real-world scenarios with prevalent environmental uncertainties and has been a long-standing object of attention in the community, without a standardized set of benchmarks. This contribution endeavors to fill this gap. We introduce the Robust Reinforcement Learning Suite (RRLS), a benchmark suite based on Mujoco environments. RRLS provides six continuous control tasks with two types of uncertainty sets for training and evaluation. Our benchmark aims to standardize robust reinforcement learning tasks, facilitating reproducible and comparable experiments, in particular those from recent state-of-the-art contributions, for which we demonstrate the use of RRLS. It is also designed to be easily expandable to new environments. The source code is available at hrefhttps://github.com/SuReLI/RRLShttps://github.com/SuReLI/RRLS.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 32 of its 49 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

32 of 49 references.

References · 32

  1. 2016OpenAI Gymcited by 1,736
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,427
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  5. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,204
  6. 2018DeepMind Control Suitecited by 956
  7. 2019Solving Rubik's Cube with a Robot Handcited by 734
  8. 2019RLBench: The Robot Learning Benchmark & Learning Environmentcited by 689
  9. 2019Leveraging Procedural Generation to Benchmark Reinforcement Learningcited by 483
  10. 2017Robust Adversarial Reinforcement Learningcited by 451
  11. 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 275
  12. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 238
20 more
  1. 2019Action Robust Reinforcement Learning and Applications in Continuous Controlcited by 174
  2. 2019Active Domain Randomizationcited by 170
  3. 2021Robust Reinforcement Learning on State Observations with Learned Optimal Adversarycited by 135
  4. 2019Behaviour Suite for Reinforcement Learningcited by 132
  5. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  6. 2021Sample Complexity of Robust Reinforcement Learning with a Generative Modelcited by 83
  7. 2020Robust Reinforcement Learning using Adversarial Populationscited by 66
  8. 2018Gotta Learn Fast: A New Benchmark for Generalization in RLcited by 59
  9. 2020RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learningcited by 57
  10. 2021Tactical Optimism and Pessimism for Deep Reinforcement Learningcited by 55
  11. 2023The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Modelcited by 52
  12. 2021Twice regularized MDPs and the equivalence between robustness and regularizationcited by 47
  13. 2020Robust Reinforcement Learning via Adversarial training with Langevin Dynamicscited by 45
  14. 2018Policy Transfer with Strategy Optimizationcited by 43
  15. 2020Model-based Adversarial Meta-Reinforcement Learningcited by 33
  16. 2020Scalable First-Order Methods for Robust MDPscited by 25
  17. 2023Towards Minimax Optimality of Model-based Robust Reinforcement Learningcited by 22
  18. 2022Efficient Policy Iteration for Robust Markov Decision Processes via Regularizationcited by 20
  19. 2022Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecificationcited by 11
  20. 2022Robust Reinforcement Learning with Distributional Risk-averse formulationcited by 10

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learningcited by 13
  2. 2025Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Safe and Robust Reinforcement Learning: Principles and Practice
  2. 2025Adversarial Diffusion for Robust Reinforcement Learning
  3. 2024Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model
  4. 2026Post-Hoc Robustness for Model-Based Reinforcement Learning
  5. 2022On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
  6. 2024Time-Constrained Robust MDPs
  7. 2025Hybrid Cross-domain Robust Reinforcement Learning
  8. 2022RORL: Robust Offline Reinforcement Learning via Conservative Smoothing