Exploiting the Kumaraswamy distribution in a reinforcement learning context

Frontiers in Robotics and AI, vol. 12, pp. 1589025

Abstract

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.

Authors 2

1. Davide Picchi Aachen Chair of Individualized Production (IP)

   RWTH Aachen University

   Affiliation as printed

   Chair of Individualized Production, RWTH Aachen University, Aachen, Germany

2. Sigrid Brell‐Çokcan Aachen Chair of Individualized Production (IP)

   RWTH Aachen University

   Affiliation as printed

   Chair of Individualized Production, RWTH Aachen University, Aachen, Germany