Computer Science > Machine Learning
arXiv:2603.09427 (cs)
[Submitted on 10 Mar 2026]
Title:Impact of Markov Decision Process Design on Sim-to-Real Reinforcement Learning
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Abstract:Reinforcement Learning (RL) has demonstrated strong potential for industrial process control, yet policies trained in simulation often suffer from a significant sim-to-real gap when deployed on physical hardware. This work systematically analyzes how core Markov Decision Process (MDP) design choices -- state composition, target inclusion, reward formulation, termination criteria, and environment dynamics models -- affect this transfer. Using a color mixing task, we evaluate different MDP configurations and mixing dynamics across simulation and real-world experiments. We validate our findings on physical hardware, demonstrating that physics-based dynamics models achieve up to 50% real-world success under strict precision constraints where simplified models fail entirely. Our results provide practical MDP design guidelines for deploying RL in industrial process control.
| Comments: | |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09427 [cs.LG] |
| (or arXiv:2603.09427v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09427
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View a PDF of the paper titled Impact of Markov Decision Process Design on Sim-to-Real Reinforcement Learning, by Tatjana Krau and 3 other authors
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