mlsanityData to 14 Oct 2026

PC-Gym: Benchmark Environments For Process Control Problems

arXiv 2410.22093 ↗First posted 29 Oct 2024eess.SY cs.SYcited by 10

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

Abstract (arXiv)

PC-Gym is an open-source tool for developing and evaluating reinforcement learning (RL) algorithms in chemical process control. It features environments that simulate various chemical processes, incorporating nonlinear dynamics, disturbances, and constraints. The tool includes customizable constraint handling, disturbance generation, reward function design, and enables comparison of RL algorithms against Nonlinear Model Predictive Control (NMPC) across different scenarios. Case studies demonstrate the framework's effectiveness in evaluating RL approaches for systems like continuously stirred tank reactors, multistage extraction processes, and crystallization reactors. The results reveal performance gaps between RL algorithms and NMPC oracles, highlighting areas for improvement and enabling benchmarking. By providing a standardized platform, PC-Gym aims to accelerate research at the intersection of machine learning, control, and process systems engineering. By connecting theoretical RL advances with practical industrial process control applications, offering researchers a tool for exploring data-driven control solutions.

Key facts. Cited by 10 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 19 of its 51 references that are in the map. First posted on arXiv 29 Oct 2024.

Builds on

19 of 51 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Control-Informed Reinforcement Learning for Chemical Processes24 Aug

Other references · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2017Constrained Policy Optimizationcited by 983
  5. 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 407
  6. 2018Understanding the impact of entropy on policy optimizationcited by 176
  7. 2023Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAXcited by 58
  8. 2020OR-Gym: A Reinforcement Learning Library for Operations Research Problemscited by 51
  9. 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learningcited by 51
  10. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 24
  11. 2020Chance Constrained Policy Optimization for Process Control and Optimizationcited by 14
  12. 2021Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learningcited by 13
6 more
  1. 2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 12
  2. 2021Safe Chance Constrained Reinforcement Learning for Batch Process Controlcited by 11
  3. 2020Constrained Model-Free Reinforcement Learning for Process Optimizationcited by 9
  4. 2015Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraintscited by 8
  5. 2021DiffLoop: Tuning PID controllers by differentiating through the feedback loopcited by 5
  6. 2023Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithmscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 6.

10 papers.

  1. 2025TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Modelscited by 3
  2. 2025Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correctioncited by 2
  3. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineeringcited by 2
  4. 2025Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learningcited by 1
  5. 2026To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Controlcited by 0
  6. 2026When (and How) to Trust the Expert: Diagnosing Query-Time Expert-Guided Reinforcement Learningcited by 0
  7. 2026Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processescited by 0
  8. 2026Numbers Beat Words: A Rigorous On-Premise Benchmark for Coupled MIMO Controller Tuningcited by 0
  9. 2026ChemWorld: Programmable Chemical Worlds for Controlled and Replayable Agent Experimentationcited by 0
  10. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Optimizing Operation Recipes with Reinforcement Learning for Safe and Interpretable Control of Chemical Processes
  2. 2025Sample-Efficient Reinforcement Learning of Koopman eNMPC
  3. 2026Impact of Markov Decision Process Design on Sim-to-Real Reinforcement Learning
  4. 2025Nuclear Microreactor Control with Deep Reinforcement Learning
  5. 2024Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
  6. 2025SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems
  7. 2026Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
  8. 2024Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization