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Exploiting the Kumaraswamy distribution in a reinforcement learning context
Davide Picchi1, Sigrid Brell-Çokcan1
1Chair of Individualized Production, RWTH Aachen University, Aachen, Germany.
Frontiers in Robotics and AI
|November 17, 2025
Summary
This study explores using the Kumaraswamy distribution for AI-controlled mini cranes. Results show it offers computational benefits and robust performance in Reinforcement Learning (RL) for continuous control tasks.
Area of Science:
- Robotics and Artificial Intelligence
- Machine Learning and Control Systems
Background:
- Mini cranes are essential in construction, with AI and Reinforcement Learning (RL) showing promise for automation.
- Current RL agents often use a squashed Gaussian distribution for action selection.
Purpose of the Study:
- Investigate the potential of AI automation in mini-crane operations.
- Evaluate replacing the traditional Gaussian distribution with the Kumaraswamy distribution for action stochastic selection in RL.
Main Methods:
- Developed an AI agent for a mini-crane scenario.
- Implemented and compared the Kumaraswamy distribution against the Gaussian distribution for action selection in RL.
Main Results:
- The Kumaraswamy distribution demonstrated computational advantages.
- Robust performance was maintained when using the Kumaraswamy distribution.
Conclusions:
- The Kumaraswamy distribution is a viable and advantageous alternative to the Gaussian distribution for RL in continuous control.
- This research supports the future real-world deployment of AI-automated mini cranes.
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