# Multiobjective Reinforcement Learning for Reconfigurable Adaptive Optimal Control of Manufacturing Processes

arXiv: https://arxiv.org/abs/1809.06750 · HTML: https://mlsanity.com/paper/1809.06750/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2204.04988/) (2022)
- [Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization](https://mlsanity.com/paper/2406.06184/) (2024)

## Abstract (arXiv)

In industrial applications of adaptive optimal control often multiple contrary objectives have to be considered. The weights (relative importance) of the objectives are often not known during the design of the control and can change with changing production conditions and requirements. In this work a novel model-free multiobjective reinforcement learning approach for adaptive optimal control of manufacturing processes is proposed. The approach enables sample-efficient learning in sequences of control configurations, given by particular objective weights.