METHODS article
Front. Robot. AI
Sec. Robotic Control Systems
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1589025
This article is part of the Research TopicModel-Free Adaptive Control of Uncertain Autonomous SystemsView all articles
Exploiting the Kumaraswamy Distribution in a Reinforcement Learning Context
Provisionally accepted
Davide Picchi1,2*
Sigrid Brell-Cokcan1- 1Chair for Individualized Production, Aachen, Germany
- 2RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
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Mini cranes play a pivotal role in construction due to their versatility across numerous scenarios. Recent advancements in Reinforcement Learning (RL) have enabled agents to operate cranes in virtual environments for predetermined tasks, paving the way for future real-world deployment. Traditionally, most RL agents use a squashed Gaussian distribution to select actions. In this study, we investigate a mini-crane scenario that could potentially be fully automated by AI and explore replacing the Gaussian distribution with the Kumaraswamy distribution, a close relative of the Beta distribution, for action stochastic selection. Our results indicate that the Kumaraswamy distribution offers computational advantages while maintaining robust performance, making it an attractive alternative for RL applications in continuous control applications.
Keywords: machine learning, crane, Construction, reinforcement learning, Kumaraswamy distribution
Received: 06 Mar 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Picchi and Brell-Cokcan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Davide Picchi, picchi@ip.rwth-aachen.de
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