# Reinforcement Learning for Batch Bioprocess Optimization

arXiv: https://arxiv.org/abs/1904.07292 · HTML: https://mlsanity.com/paper/1904.07292/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 28 of 28)

- [Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning](https://mlsanity.com/paper/2111.07171/) (2021)
- [Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach](https://mlsanity.com/paper/2203.09661/) (2022)
- [Safe Chance Constrained Reinforcement Learning for Batch Process Control](https://mlsanity.com/paper/2104.11706/) (2021)
- [Control-Informed Reinforcement Learning for Chemical Processes](https://mlsanity.com/paper/2408.13566/) (2024)
- [Machine learning for industrial sensing and control: A survey and practical perspective](https://mlsanity.com/paper/2401.13836/) (2024)
- [A Meta-Reinforcement Learning Approach to Process Control](https://mlsanity.com/paper/2103.14060/) (2021)
- [Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes](https://mlsanity.com/paper/2111.05589/) (2021)
- [Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control](https://mlsanity.com/paper/2308.02765/) (2023)
- [Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes](https://mlsanity.com/paper/2105.06543/) (2021)
- [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022)
- [Machine learning in bioprocess development: From promise to practice](https://mlsanity.com/paper/2210.02200/) (2022)
- [Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses](https://mlsanity.com/paper/2503.22409/) (2025)
- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [Human-Algorithm Collaborative Bayesian Optimization for Engineering Systems](https://mlsanity.com/paper/2404.10949/) (2024)
- [Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning](https://mlsanity.com/paper/2212.04136/) (2022)
- [Enhancing reinforcement learning for population setpoint tracking in co-cultures](https://mlsanity.com/paper/2411.09177/) (2024)
- [Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)](https://mlsanity.com/paper/2508.16474/) (2025)
- [Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering](https://mlsanity.com/paper/2510.24272/) (2025)
- [Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data](https://mlsanity.com/paper/2211.14493/) (2022)
- [Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation](https://mlsanity.com/paper/2410.18864/) (2024)
- [Reinforcement learning for robust dynamic metabolic control](https://mlsanity.com/paper/2504.00735/) (2025)
- [Reinforcement learning meets bioprocess control through behaviour cloning: Real-world deployment in an industrial photobioreactor](https://mlsanity.com/paper/2509.06853/) (2025)
- [TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control](https://mlsanity.com/paper/2204.10685/) (2022)
- [Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives](https://mlsanity.com/paper/2404.00247/) (2024)
- [Sensitivity Analysis on Policy-Augmented Graphical Hybrid Models with Shapley Value Estimation](https://mlsanity.com/paper/2411.13396/) (2024)
- [Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning](https://mlsanity.com/paper/2505.21026/) (2025)
- [Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review](https://mlsanity.com/paper/2510.21758/) (2025)
- [Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling](https://mlsanity.com/paper/2607.20539/) (2026)

## Abstract (arXiv)

Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and stochastic behaviours. Furthermore, biological systems are highly complex, therefore plant-model mismatch is often present. To address the aforementioned challenges we propose a Reinforcement learning based optimization strategy for batch processes. In this work, we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network. We assume that a preliminary process model is available, which is exploited to obtain a preliminary optimal control policy. Subsequently, this policy is updatedbased on measurements from thetrueplant. The capabilities of our proposed approach were tested on three case studies (one of which is nonsmooth) using a more complex process model for thetruesystemembedded with adequate process disturbance. Lastly, we discussed the advantages and disadvantages of this strategy compared against current existing approaches such as nonlinear model predictive control.