The safety of autonomous systems has become an important concern in recent years. A new
mechanism for developing safe and reliable autonomous systems is a very challenging task …

The advent of advanced computational technologies and artificial intelligence has ushered
in a new era of complex systems and applications, notably in the realms of autonomous …

This paper explores the advancements in safe and robust reinforcement learning (RL),
addressing the challenges and solutions associated with ensuring reliability and safety in …

Abstract Machine learning (ML) algorithms allow computers to learn without being explicitly
programmed. Their utilization is spreading to highly sophisticated tasks across multiple …

Reinforcement learning, often known as RL, has developed as a strong paradigm to teach
autonomous software agents to make choices in contexts that are both complicated and …

Abstract Machine Learning algorithms are becoming more prevalent in critical systems
where dynamic decision making and efficiency are the goal. As is the case for complex and …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Reinforcement Learning (RL) algorithms show amazing performance in recent years, but
placing RL in real-world applications such as self-driven vehicles may suffer safety …

Reinforcement learning (RL) has proved a prominent success in the game of Go, video
games, navigation, recommendation systems and other fields. However, a large number of …

Upcoming technological innovations and findings are immense in the field of intelligent
transport systems (ITS). Cyber-physical systems (CPSs) are complex systems that integrate …