## Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards

### Huiwen Jia · Cong Shi · Siqian Shen

##### Hall E #1310

Keywords: [ T: Optimization ] [ T: Probabilistic Methods ] [ T: Online Learning and Bandits ]

[ Abstract ]
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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT

Spotlight presentation: T: Online Learning and Bandits/Learning Theory
Wed 20 Jul 10:15 a.m. PDT — 11:45 a.m. PDT

Abstract: We consider a price-based revenue management problem with reusable resources over a finite time horizon $T$. The problem finds important applications in car/bicycle rental, ridesharing, cloud computing, and hospitality management. Customers arrive following a price-dependent Poisson process and each customer requests one unit of $c$ homogeneous reusable resources. If there is an available unit, the customer gets served within a price-dependent exponentially distributed service time; otherwise, she waits in a queue until the next available unit. The decision maker assumes that the inter-arrival and service intervals have an unknown linear dependence on a $d_f$-dimensional feature vector associated with the posted price. We propose a rate-optimal online learning and pricing algorithm, termed Batch Linear Confidence Bound (BLinUCB), and prove that the cumulative regret is $\tilde{O}( d_f \sqrt{T } )$. In establishing the regret, we bound the transient system performance upon price changes via a coupling argument, and also generalize linear bandits to accommodate sub-exponential rewards.

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