R. Zuo, S. Khan, Z. Wang, G. E. Katz and Q. Qiu, Beyond VisionMask: Explaining Reinforcement Learning Via Contrastive-State Masking, IEEE Intelligent Systems, vol. 41, no. 4, pp. 56-63, July-Aug. 2026,10.1109/MIS.2026.3683960.
Existing explainable reinforcement learning (RL) approaches often fail to capture the contrastive nature of human reasoning—answering “why this action instead of that one?”. To address this challenge, we present Continuous VisionMask (cVM), a contrastive learning framework that explains RL agents in continuous state and action spaces. cVM extends our prior work, VisionMask, which was restricted to vision-based discrete settings, by introducing a quantized representation of continuous state and a set of contrastive learning objectives. This generalization enables cVM to provide faithful, robust, and sparse attributions highlighting which aspects of the sensed input are most important for the agent’s decision making, and to do so across a broader range of real-world RL scenarios. We evaluate cVM in multiple continuous-control environments and compare it against existing explainability baselines. Our results demonstrate that cVM produces more faithful and stable explanations, thereby enhancing transparency and interpretability in continuous RL systems.