# Exploiting the Kumaraswamy distribution in a reinforcement learning context

## TL;DR

This paper explores using the Kumaraswamy distribution in reinforcement learning for mini-crane automation, showing it offers computational benefits without sacrificing performance.

## Contribution

The novel use of the Kumaraswamy distribution in RL for continuous control tasks is proposed and validated.

## Key findings

- The Kumaraswamy distribution provides computational advantages over Gaussian distributions in RL.
- It maintains robust performance in continuous control tasks like mini-crane automation.
- This distribution is a viable alternative for action selection in reinforcement learning.

## 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.