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 …

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 …

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 …

This paper presents BPpy, a novel framework for behavioral programming (BP) in Python.
Designed with a flexible architecture, BPpy is crafted for easy integration with various Python …

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 …

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 …

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

强化学习(Reinforcement learning, RL) 在围棋, 视频游戏, 导航, 推荐系统等领域均取得了巨大
成功. 然而, 许多强化学习算法仍然无法直接移植到真实物理环境中. 这是因为在模拟场景下智能 …

Deep neural networks (DNNs) have become a crucial instrument in the software
development toolkit, due to their ability to efficiently solve complex problems. Nevertheless …