Sequential decision-making processes (SDPs) are fundamental for complex real-world
challenges, such as autonomous driving, robotic control, and traffic management. While …

In sequential decision making, neural networks (NNs) are nowadays commonly used to
represent and learn the agent's policy. This area of application has implied new software …

Testing is a promising way to gain trust in learned action policies π. Prior work on action-
policy testing in AI planning formalized bugs as states t where π is sub-optimal with respect …

Testing was recently proposed as a method to gain trust in learned action policies in
classical planning. Test cases in this setting are states generated by a fuzzing process that …

Reinforcement Learning is the premier technique to approach sequential decision problems,
including complex tasks such as driving cars and landing spacecraft. Among the software …

In recent years, following tremendous achievements in Reinforcement Learning, a great
deal of interest has been devoted to ML models for sequential decision-making. Together …

Deep learning is increasingly used to learn policies for planning problems. However,
policies represented by neural networks are difficult to interpret, verify and trust. Existing …

When using action policies for sequential decision making, testing is a valuable tool to gain
trust in their decisions. In particular, the action policies obtained via training deep neural …

Neural networks (NN) are an increasingly important representation of action policies. With
their application for realtime decision-making in safety critical areas, like, eg, autonomous …