  Recent years have witnessed the increasing interests in research of
crowdfunding mechanism. In this area, dynamics tracking is a significant issue
but is still under exploration. Existing studies either fit the fluctuations of
time-series or employ regularization terms to constrain learned tendencies.
However, few of them take into account the inherent decision-making process
between investors and crowdfunding dynamics. To address the problem, in this
paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3)
algorithm to predict the funding progress in crowdfunding. Specifically,
actor-critic frameworks are employed to model the relationship between
investors and campaigns, where all of the investors are viewed as an agent that
could interact with the environment derived from the real dynamics of
campaigns. Then, to further explore the in-depth implications of patterns
(i.e., typical characters) in funding series, we propose to subdivide them into
$\textit{fast-growing}$ and $\textit{slow-growing}$ ones. Moreover, for the
purpose of switching from different kinds of patterns, the actor component of
TC3 is extended with a structure of options, which comes to the TC3-Options.
Finally, extensive experiments on the Indiegogo dataset not only demonstrate
the effectiveness of our methods, but also validate our assumption that the
entire pattern learned by TC3-Options is indeed the U-shaped one.
