… on deep reinforcement learning (RL), we design an adaptive car-following trajectory control
algorithm, which is called Deep Adaptive Control, to cope with different traffic risk levels… Deep …

… There are several ways these uncertainty estimates could be included into a reinforcement
… different roles played by both uncertainties, we propose a simple uncertainty-aware Deep Q …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

… estimate the concrete probabilities at different risk levels. The three risk levels used in this
study … In this study, it is assumed that different risk levels have the same prior probability with a …

… This risk-seeking value can be seen as a utility for the agent under a certain risk profile
where c specifies the level of risk. For \(c>0\), the agent is risk-seeking and values risky states …

… In performance-based methodology, performance indicators are established and a certain
performance is ensured under various hazard levels instead of following a set of rules set by …

… and risk aversion level. Again to ensure somehow some Markovianity and to include in the
current knowledge of the virtual agent more than the last observation of these features, we …

… level a Deep Reinforcement Learning (DRL) agent selects among a number of discrete
actions, representing low-level … Deep reinforcement learning for automated stock trading: An …

… collision risk. The COLREGs are in place to reduce collision risk and indirectly affect the risk
level by … between the rules and the risk level, employing a measure of risk as a proxy for the …

… may jointly lead to different system-level failures or reduction … exists various potential
intervention actions with different … , deep reinforcement learning (DRL), for adaptive and risk-…