# Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses

arXiv: https://arxiv.org/abs/2503.22409 · HTML: https://mlsanity.com/paper/2503.22409/

## 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 5 of its 34 references that are in the map.
- First posted on arXiv 28 Mar 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, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC](https://mlsanity.com/paper/2203.13671/) (2022)
- [Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis](https://mlsanity.com/paper/2401.00670/) (2024)
- [Enhancing reinforcement learning for population setpoint tracking in co-cultures](https://mlsanity.com/paper/2411.09177/) (2024)

## Built on by (most cited first, 4 of 4)

- [Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering](https://mlsanity.com/paper/2510.24272/) (2025)
- [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)
- [Switching-time bioprocess control with pulse-width-modulated optogenetics](https://mlsanity.com/paper/2511.22893/) (2025)

## Abstract (arXiv)

Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-learning framework for multi-setpoint and multi-trajectory tracking. Tracking multiple setpoints and time-varying trajectories in reinforcement learning is challenging due to the complexity of balancing multiple objectives, a difficulty further exacerbated by system uncertainties such as uncertain initial conditions and stochastic dynamics. This challenge is relevant, e.g., in bioprocesses involving microbial consortia, where precise control over population compositions is required. We introduce a novel return function based on multiplicative reciprocal saturation functions, which explicitly couples reward gains to the simultaneous satisfaction of multiple references. Through a case study involving light-mediated cybergenetic growth control in microbial consortia, we demonstrate via computational experiments that our approach achieves faster convergence, improved stability, and superior control compliance compared to conventional quadratic-cost-based return functions. Moreover, our method enables tuning of the saturation function's parameters, shaping the learning process and policy updates. By incorporating system uncertainties, our framework also demonstrates robustness, a key requirement in industrial bioprocessing. Overall, this work advances reinforcement-learning-based control strategies in bioprocess engineering, with implications in the broader field of process and systems engineering.