This article presents a literature review of the past five years of studies using Deep
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …

Deep learning (DL), which includes deep reinforcement learning (DRL), holds great promise
for carrying out real-world tasks that human minds seem to cope with quite readily. That …

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …

In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach
to solving real-world tasks. However, despite their successes, DRL-based policies suffer …

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in
machine learning due to its successful applications to real-world and complex systems …

Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating
agents that control autonomous systems. However, the “black box” nature of DRL agents …

In recent years, Machine Learning (ML) models have achieved remarkable success in
various domains. However, these models also tend to demonstrate unsafe behaviors …

The local and global interpretability of various ML models has been studied extensively in
recent years. However, despite significant progress in the field, many known results remain …

In this short paper, we present our ongoing work on the veriFIRE project—a collaboration
between industry and academia, aimed at using verification for increasing the reliability of a …

Deep neural networks (DNNs) have become the technology of choice for realizing a variety
of complex tasks. However, as highlighted by many recent studies, even an imperceptible …