mlsanityData to 14 Oct 2026

Control Synthesis with Reinforcement Learning: A Modeling Perspective

arXiv 2510.25063 ↗First posted 29 Oct 2025eess.SY cs.SYcited by 1

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Controllers designed with reinforcement learning can be sensitive to model mismatch. We demonstrate that designing such controllers in a virtual simulation environment with an inaccurate model is not suitable for deployment in a physical setup. Controllers designed using an accurate model is robust against disturbance and small mismatch between the physical setup and the mathematical model derived from first principles; while a poor model results in a controller that performs well in simulation but fails in physical experiments. Sensitivity analysis is used to justify these discrepancies and an empirical region of attraction estimation help us visualize their robustness.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 24 of its 55 references that are in the map. First posted on arXiv 29 Oct 2025.

Builds on

24 of 55 references.

References · 24

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  3. 2017Robust Adversarial Reinforcement Learningcited by 451
  4. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
  5. 2019Control Barrier Functions: Theory and Applicationscited by 372
  6. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  7. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 151
  8. 2018The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systemscited by 88
  9. 2019BayesSim: adaptive domain randomization via probabilistic inference for robotics simulatorscited by 85
  10. 2017Towards Generalization and Simplicity in Continuous Controlcited by 68
  11. 2021Robot Learning from Randomized Simulations: A Reviewcited by 63
  12. 2018Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learningcited by 62
12 more
  1. 2021Auto-Tuned Sim-to-Real Transfercited by 49
  2. 2021How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Reviewcited by 39
  3. 2025Symmetry in Neural Network Parameter Spacescited by 36
  4. 2022Lyapunov Design for Robust and Efficient Robotic Reinforcement Learningcited by 28
  5. 2020Chance Constrained Policy Optimization for Process Control and Optimizationcited by 14
  6. 2023Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robotscited by 8
  7. 2023Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimizationcited by 7
  8. 2023Symmetry-enforcing neural networks with applications to constitutive modelingcited by 5
  9. 2025Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policiescited by 4
  10. 2023Examining the simulation-to-reality gap of a wheel loader digging in deformable terraincited by 3
  11. 2023AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Imagescited by 3
  12. 2023Tight Certified Robustness via Min-Max Representations of ReLU Neural Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole Systemcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Dual Action Policy for Robust Sim-to-Real Reinforcement Learning
  2. 2024Safe and Robust Reinforcement Learning: Principles and Practice
  3. 2021Revisiting Design Choices in Offline Model-Based Reinforcement Learning
  4. 2024Constrained Reinforcement Learning Under Model Mismatch
  5. 2023Model-based Offline Reinforcement Learning with Local Misspecification
  6. 2026All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning
  7. 2026Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
  8. 2024ASID: Active Exploration for System Identification in Robotic Manipulation