mlsanityData to 14 Oct 2026

A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants

arXiv 2401.13020 ↗First posted 23 Jan 2024eess.SY cs.LG cs.SYcited by 4

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Traditional control theory-based methods require tailored engineering for each system and constant fine-tuning. In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly. Model-free Reinforcement learning (RL) has emerged as a promising solution for control tasks due to its ability to learn from trial-and-error interactions with the environment. It eliminates the need for explicitly modeling the environment's dynamics, which is potentially inaccurate. However, the direct imposition of state constraints in power plant control raises challenges for standard RL methods. To address this, we propose a chance-constrained RL algorithm based on Proximal Policy Optimization for supervisory control. Our method employs Lagrangian relaxation to convert the constrained optimization problem into an unconstrained objective, where trainable Lagrange multipliers enforce the state constraints. Our approach achieves the smallest distance of violation and violation rate in a load-follow maneuver for an advanced Nuclear Power Plant design.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 47 references that are in the map. First posted on arXiv 23 Jan 2024.

Builds on

14 of 47 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  6. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  7. 2018Reward Constrained Policy Optimizationcited by 417
  8. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  9. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
  10. 2018Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learningcited by 62
  11. 2021Survey on reinforcement learning for language processingcited by 31
  12. 2023Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Drivingcited by 6
2 more
  1. 2023Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learningcited by 5
  2. 2022Design of a Supervisory Control System for Autonomous Operation of Advanced Reactorscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modelingcited by 5
  2. 2024A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applicationscited by 1
  3. 2026Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observabilitycited by 0
  4. 2026Autopilot-Preserving Residual Q-Learning with HJB-Inspired Finite-Action Risk Filtering for Fixed-Wing UAV Command Supervisioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems
  2. 2024Safe Reinforcement Learning for Power System Control: A Review
  3. 2025Safe Reinforcement Learning-based Automatic Generation Control
  4. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  5. 2026The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
  6. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
  7. 2025Wide-Area Feedback Control for Renewables-Heavy Power Systems: A Comparative Study of Reinforcement Learning and Lyapunov-Based Design
  8. 2026Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources