Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …

Environmental sustainability is a worldwide key challenge attracting increasing attention due
to climate change, pollution, and biodiversity decline. Reinforcement learning, initially …

Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for
tasks where complex policies are learned within reactive systems. Unfortunately, these …

Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide
variety of robotic applications. A natural consequence is the adoption of this paradigm for …

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 Neural Networks are increasingly adopted in critical tasks that require a high level of
safety, eg, autonomous driving. While state-of-the-art verifiers can be employed to check …

Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL).
However, the cost is typically encoded as an indicator function due to the difficulty of …

Learning agents can make use of Reinforcement Learning (RL) to decide their actions by
using a reward function. However, the learning process is greatly influenced by the elect of …

Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-
world scenarios. Recently, verification approaches have been proposed to allow quantifying …

Deep Policy Gradient (PG) algorithms employ value networks to drive the learning of
parameterized policies and reduce the variance of the gradient estimates. However, value …