Neural Information Processing Systems http://nips.cc/

Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision making procedures.

In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred.

Neural Information Processing Systems http://nips.cc/

Instead,ourboundrequires asimple, intuitive condition which is well justified by prior empirical works and holds in practiceeffectively100%ofthetime. Theboundisinspiredby H H-divergence but is easier to evaluate and substantially tighter, consistently providing nonvacuous test error upper bounds.

Differentsamplingapproaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees.

Recall therealoptimal n (optimal P) and n isdenoted n( ). Withac0 and solve Eq. 10 exactlyby29].