Exploring counterfactuals in continuous-action reinforcement learning

Reinforcement learning (RL) agents are capable of making complex decisions in dynamic environments, yet their behavior often remains opaque. When an agent executes a sequence of actions--such as administering insulin to a diabetic patient or controlling a spacecraft's landing--it is rarely clear how outcomes might have changed under alternative choices. This challenge becomes particularly pronounced in settings involving continuous action spaces, where decisions are not confined to discrete options but span a spectrum of real-valued magnitudes. The framework introduced in recent work aims to generate counterfactual explanations in such settings, offering a structured approach to explore "what if" scenarios. The value of counterfactual reasoning in RL becomes apparent in scenarios with high-stakes, temporally extended consequences.