# Reinforcement learning meets bioprocess control through behaviour cloning: Real-world deployment in an industrial photobioreactor

arXiv: https://arxiv.org/abs/2509.06853 · HTML: https://mlsanity.com/paper/2509.06853/

## Key facts

- Cited by 1 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 7 of its 51 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [Machine learning for industrial sensing and control: A survey and practical perspective](https://mlsanity.com/paper/2401.13836/) (2024)
- [Learning to control inexact Benders decomposition via reinforcement learning](https://mlsanity.com/paper/2508.06700/) (2025)
- [Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses](https://mlsanity.com/paper/2503.22409/) (2025)

## Built on by (most cited first, 1 of 1)

- [Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering](https://mlsanity.com/paper/2510.24272/) (2025)

## Abstract (arXiv)

The inherent complexity of living cells as production units creates major challenges for maintaining stable and optimal bioprocess conditions, especially in open Photobioreactors (PBRs) exposed to fluctuating environments. To address this, we propose a Reinforcement Learning (RL) control approach, combined with Behavior Cloning (BC), for pH regulation in open PBR systems. This represents, to the best of our knowledge, the first application of an RL-based control strategy to such a nonlinear and disturbance-prone bioprocess. Our method begins with an offline training stage in which the RL agent learns from trajectories generated by a nominal Proportional-Integral-Derivative (PID) controller, without direct interaction with the real system. This is followed by a daily online fine-tuning phase, enabling adaptation to evolving process dynamics and stronger rejection of fast, transient disturbances. This hybrid offline-online strategy allows deployment of an adaptive control policy capable of handling the inherent nonlinearities and external perturbations in open PBRs. Simulation studies highlight the advantages of our method: the Integral of Absolute Error (IAE) was reduced by 8% compared to PID control and by 5% relative to standard off-policy RL. Moreover, control effort decreased substantially-by 54% compared to PID and 7% compared to standard RL-an important factor for minimizing operational costs. Finally, an 8-day experimental validation under varying environmental conditions confirmed the robustness and reliability of the proposed approach. Overall, this work demonstrates the potential of RL-based methods for bioprocess control and paves the way for their broader application to other nonlinear, disturbance-prone systems.