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...

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 a long-standing gap between inventory theory and practice, we study online inventory models with unknown cyclic demand distributions. We design efficient bandits-atop-reinforcement-learning algorithms that cater to the structure of inventory problems. We apply the standard...

In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice sellers often face business constraints that prevent them from conducting...

We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation where the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then...

We study a single product pricing problem with demand censoring in an offline data-driven setting. In this problem, a retailer is given a finite amount of inventory, and faces a random demand that is price sensitive in a linear fashion with unknown price sensitivity and base demand distribution....