# Chance Constrained Policy Optimization for Process Control and Optimization

arXiv: https://arxiv.org/abs/2008.00030 · HTML: https://mlsanity.com/paper/2008.00030/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Safe Chance Constrained Reinforcement Learning for Batch Process Control](https://mlsanity.com/paper/2104.11706/) (2021)
- [PC-Gym: Benchmark Environments For Process Control Problems](https://mlsanity.com/paper/2410.22093/) (2024)
- [Control-Informed Reinforcement Learning for Chemical Processes](https://mlsanity.com/paper/2408.13566/) (2024)
- [Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes](https://mlsanity.com/paper/2111.05589/) (2021)
- [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022)
- [Neural ODEs as Feedback Policies for Nonlinear Optimal Control](https://mlsanity.com/paper/2210.11245/) (2022)
- [Hierarchical RL-MPC for Demand Response Scheduling](https://mlsanity.com/paper/2502.13714/) (2025)
- [Distributional Reinforcement Learning for Scheduling of Chemical Production Processes](https://mlsanity.com/paper/2203.00636/) (2022)
- [Distributional constrained reinforcement learning for supply chain optimization](https://mlsanity.com/paper/2302.01727/) (2023)
- [Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering](https://mlsanity.com/paper/2510.24272/) (2025)
- [Control Synthesis with Reinforcement Learning: A Modeling Perspective](https://mlsanity.com/paper/2510.25063/) (2025)
- [Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs](https://mlsanity.com/paper/2401.12243/) (2024)
- [Bayesian Optimization of Partially Known Systems using Hybrid Models](https://mlsanity.com/paper/2603.11199/) (2026)
- [Addressing Terminal Constraints in Data-Driven Demand Response Scheduling](https://mlsanity.com/paper/2605.14741/) (2026)

## Abstract (arXiv)

Chemical process optimization and control are affected by 1) plant-model mismatch, 2) process disturbances, and 3) constraints for safe operation. Reinforcement learning by policy optimization would be a natural way to solve this due to its ability to address stochasticity, plant-model mismatch, and directly account for the effect of future uncertainty and its feedback in a proper closed-loop manner; all without the need of an inner optimization loop. One of the main reasons why reinforcement learning has not been considered for industrial processes (or almost any engineering application) is that it lacks a framework to deal with safety critical constraints. Present algorithms for policy optimization use difficult-to-tune penalty parameters, fail to reliably satisfy state constraints or present guarantees only in expectation. We propose a chance constrained policy optimization (CCPO) algorithm which guarantees the satisfaction of joint chance constraints with a high probability - which is crucial for safety critical tasks. This is achieved by the introduction of constraint tightening (backoffs), which are computed simultaneously with the feedback policy. Backoffs are adjusted with Bayesian optimization using the empirical cumulative distribution function of the probabilistic constraints, and are therefore self-tuned. This results in a general methodology that can be imbued into present policy optimization algorithms to enable them to satisfy joint chance constraints with high probability. We present case studies that analyze the performance of the proposed approach.