Question about the "sample" in "Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation" I'm reading this paper recently. I'm confused about the "sample a distribution of trajectories" repeatedly mentioned in the article. I don't understand what this means. What is the sample often mentioned in the article and how to operate it?
Does anyone understand that? Thanks!

In the paper, the state space represents the space of molecules, but the only movement directions allowed are additive edits. This creates a Directed Acyclic Graph, since after doing an edit the "agent" is not allowed to go backward.
Thus to construct a molecule, we can use the notion of an "episode" as in Reinforcement Learning. In this state space, there is a unique state (e.g. in the paper "the empty molecule") from which all other states can be reached. This state ($s_0$) is used as the starting point of an episode, after which actions are taken (additive edits) until the agent says "stop" or reaches a point where no other actions are possible (e.g. the molecule reached some maximum allowed size).
This sequence of events is referred to as an episode, and the mathematical object that holds the sequence of states and actions traversed $\tau = (s_0, a_0, s_1, ..., a_{T-1}, s_T)$ is called a trajectory.
Disclaimer, I am the first author of this paper.
