The paper Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks introduces a novel reinforcement learning-based approach addressing the Deceptive Path Planning (DPP) problem by devising paths invisible to observers. Incorporating Graph Neural Networks, the presented method adapts deceptively to changing environments in real-time. The results demonstrate successful generalization without additional fine-tuning, scalability, tunable deception levels, and instant adaptability to environmental variations - all crucial for DPP’s practical applications.