Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks.

However, inmanyapplications including contractual hiringinonlinefreelancing platforms and server allocation in cloud computing systems, the outcome of each action is observed only after a random and action-dependent time.

Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.