We study an adversarial online assortment problem with reusable resources, under the setting of customer-dependent usage durations. The decision maker (DM) offers assortments of the reusable resources (also referred to as products) to a sequence of online customers, and we first study the case...

In this paper, we study an adversarial online reusable resource allocation problemwith multi-class arrivals, where each class is associated with its price per unit time and usage duration. Each unit of resource will be returned after the usage duration of the class of customer that the unit has...

We study a general problem of allocating limited resources to heterogeneous customers over time, under model uncertainty. Each type of customer can be serviced using different actions, each of which stochastically consumes some combination of resources, and returns different rewards for the...

Motivated by applications in inventory control and real-time bidding, we consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts. In this setting, both the reward and state transition distributions are allowed to evolve over time, as long as...