Decision trees are traditionally trained using greedy heuristics that locally optimize an
impurity or information metric. Recently there has been a surge of interest in optimal …

Global optimization of decision trees has shown to be promising in terms of accuracy, size,
and consequently human comprehensibility. However, many of the methods used rely on …

Decision trees have gained popularity as interpretable surrogate models for learning-based
control policies. However, providing safety guarantees for systems controlled by decision …

Dealing with aleatoric uncertainty is key in many domains involving sequential decision
making, eg, planning in AI, network protocols, and symbolic program synthesis. This paper …

Deep reinforcement learning (DRL) has garnered remarkable success across various
domains, propelled by advancements in deep learning (DL) technologies. However, the …

Markov decision processes (MDPs) provide a fundamental model for sequential decision
making under process uncertainty. A classical synthesis task is to compute for a given MDP …

Reinforcement learning techniques leveraging deep learning have made tremendous
progress in recent years. However, the complexity of neural networks prevents practitioners …

We propose Constraint-Generation Policy Optimization (CGPO) for optimizing policy
parameters within compact and interpretable policy classes for mixed discrete-continuous …

Markov decision processes (MDPs) describe sequential decision-making processes; MDP
policies return for every state in that process an advised action. Classical algorithms can …

Decision trees, owing to their interpretability, are attractive as control policies for (dynamical)
systems. Unfortunately, constructing, or synthesising, such policies is a challenging task …