Abstract Deep Reinforcement Learning (DRL) has demonstrated its strength in developing
intelligent systems. These systems shall be formally guaranteed to be trustworthy when …

Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

Formally verifying deep reinforcement learning (DRL) systems suffers from both inaccurate
verification results and limited scalability. The major obstacle lies in the large overestimation …

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 …

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 …

Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …

Deep reinforcement learning has been successfully applied to many control tasks, but the
application of such controllers in safety-critical scenarios has been limited due to safety …

Deep neural networks (DNNs) have gained significant popularity in recent years, becoming
the state of the art in a variety of domains. In particular, deep reinforcement learning (DRL) …

Deep reinforcement learning is an increasingly popular technique for synthesising policies
to control an agent's interaction with its environment. There is also growing interest in …

Reinforcement Learning (RL) is a widely employed machine learning architecture that has
been applied to a variety of decision-making problems, from resource management to robot …