In order to perform a wide search over the space of the trees, it is critical to solve this projection problem fast.

While therewardcompensation mechanism isunknown,the learner can adapt his (her) decision to the past reward feedback so as to maximize the sum of rewards.

This is meaningful because in most applications, the time complexities for evaluating gradients at different points are of the same magnitude.

PageRank (PR), an algorithm originally proposed by Page et al. for ranking web-pages [1] has found manysuccessful applications, including community detection [2,3],linkprediction [4]and recommendersystemdesign[5,6].

Namely,thattheempirical lossofall the functions in the class is -close to the true loss. Finally, we develop a set of tools for calculating the approximate description length of classes of functions that can be presented as a composition of linear function classes and non-linear functions.

B.1 Learningdetails All of our code and experiments are implemented in Python using the PyTorch [5]deep learning framework.

Our main result is a matching upper and lower bound for the secretary ranking problem.

We need some more notation in order to linearize the value function.

Many potential applications of reinforcement learning (RL) have intractably-large state spaces.

The algorithm requires solving a non-convex optimization problem inthespace ofprobability measures, thatrequires delicate analysis.