Reviews: Deep Reinforcement Learning of Marked Temporal Point Processes

The paper "Deep Reinforcement Learning of Marked Temporal Point Processes" proposes a new deep neural architecture for reinforcement learning in situations where actions are taken and feedbacks are received in asynchronous continous time. This is the main novelty of the work: dealing with non discrete times and actions and feedbacks living in independent timelines. I like the proposed architecture and I think the idea can be of interest for the community. However, from my point of view several key points are missing from the paper to well understand the approach and its justification, and also for a researcher which would like to re-implement it: - For me, it would be very important to discuss more about marked temporal process. Why is it better to model time like this rather than using for instance an exponential law?