Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

This paper presents a literature review of reinforcement learning (RL) and its applications to
process control and optimization. These applications were evaluated from a new …

Some of the most successful applications of deep reinforcement learning to challenging
domains in discrete and continuous control have used policy gradient methods in the on …

QDax is an open-source library with a streamlined and modular API for Quality-Diversity
(QD) optimisation algorithms in Jax. The library serves as a versatile tool for optimisation …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

In complex reinforcement learning (RL) problems, policies with similar rewards may have
substantially different behaviors. It remains a fundamental challenge to optimize rewards …

In histopathology practice, scanners, tissue processing, staining, and image acquisition
protocols vary from center to center, resulting in subtle variations in images. Vanilla …

We present a general framework for training safe agents whose naive incentives are unsafe.
As an example, manipulative or deceptive behaviour can improve rewards but should be …

Deep learning is an obvious method for the detection of disease, analyzing medical images
and many researchers have looked into it. However, the performance of deep learning …

The field of automated machine learning (AutoML) has gained significant attention in recent
years due to its ability to automate the process of building and optimizing machine learning …