Reviews: Scalable Planning with Tensorflow for Hybrid Nonlinear Domains

This paper documents an approach to planning in domains with hybrid state and action spaces, using efficient stochastic gradient descent methods, in particular, in this case, as implemented in TensorFlow. Certainly, the idea of optimizing plans or trajectories using gradient methods is not new (lots of literature on shooting methods, using fmincon, etc. exists). And, people have long understood that random restarts for gradient methods in non-linear problems are a way to mitigate problems with local optimal. What this paper brings is (1) the idea of running those random restarts in parallel and (b) using an existing very efficient implementation of SGD. I'm somewhat torn, because it seems like a good idea, but also not very surprising.