Keywords
rollback update
gradient noise
synthetic control
reproducible experiment
Abstract
The cited literature on policy-gradient control ranges from Model-free Control Design Using Policy Gradient Reinforcement Learning in LPV Framework to Quadratic exponential decrease roll-back: An efficient gradient update mechanism in proximal… and Policy Gradient Reinforcement Learning for Parameterized Continuous-Time Optimal Control, bringing together methods that are often evaluated under incompatible assumptions. A reference-grounded synthesis is developed through model-free control, robustness evidence, and proximal constraints. The comparison distinguishes algorithmic contribution from the evidence used to support reliability, transferability, or practical use. This perspective clarifies which conclusions travel across contexts and which remain tied to particular data or procedures. Future studies can build on the map through preregistered comparisons, sensitivity analysis, and openly documented evaluation choices.
References
Zhao, R., Xu, D., Jian, S., Tan, T., Sun, X., & Zhang, W. (2023). Quadratic exponential decrease roll-back: An efficient gradient update mechanism in proximal policy optimization. In 2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) (pp. 65-70). IEEE. https://doi.org/10.1109/MLCCIM60412.2023.00015
de Miguel Gomez, A., & Toosi, F. (2021). Continuous Parameter Control in Genetic Algorithms using Policy Gradient Reinforcement Learning. Proceedings of the 13th International Joint Conference on Computational Intelligence, 115-122. https://doi.org/10.5220/0010643500003063
wani, M.-A., Mohd., M., Khanday, H.-A., & Bhat, W.-A. (2025). Kl-Regularized Stochastic Policy Gradient for Stable Continuous Control in Off-Policy Reinforcement Learning. . https://doi.org/10.2139/ssrn.5357499
Yang, X., Zhang, H., & Wang, Z. (2021). Policy Gradient Reinforcement Learning for Parameterized Continuous-Time Optimal Control. 2021 33rd Chinese Control and Decision Conference (CCDC), 59-64. https://doi.org/10.1109/ccdc52312.2021.9602755
Liu, G., Chen, G., & Huang, V. (2023). Policy ensemble gradient for continuous control problems in deep reinforcement learning. Neurocomputing, 548, 126381. https://doi.org/10.1016/j.neucom.2023.126381
Sadeghi Tabas, S., & Samadi, V. (2022). Sustainable Reservoir Operation and Control Using a Deep Reinforcement Learning Policy Gradient Method. . https://doi.org/10.5194/egusphere-egu22-13011
Naha, A., & Dey, S. (2025). Policy Gradient-based Reinforcement Learning for LQG Control with Chance Constraints. 2025 European Control Conference (ECC), 364-371. https://doi.org/10.23919/ecc65951.2025.11186950
Bao, Y., & Velni, J.-M. (2021). Model-free Control Design Using Policy Gradient Reinforcement Learning in LPV Framework. 2021 European Control Conference (ECC), 150-155. https://doi.org/10.23919/ecc54610.2021.9655004
Denkert, R., Pham, H., & Warin, X. (2025). Control Randomisation Approach for Policy Gradient and Application to Reinforcement Learning in Optimal Switching. Applied Mathematics & Optimization, 91(1). https://doi.org/10.1007/s00245-024-10207-5
Liu, Y., Gou, L., Xue, Y., & Qiao, M. (2025). Optimization of Aero-engine Control Laws Based on Deep Deterministic Policy Gradient Reinforcement Learning Algorithm. 2025 16th International Conference on Mechanical and Aerospace Engineering (ICMAE), 116-121. https://doi.org/10.1109/icmae66341.2025.11277007