2 citations · 3 across the 6 of their papers we have counts for
6 papers
Simple and Robust Quality Disclosure: The Power of Quantile Partition
Shipra Agrawal, Yiding Feng, Wei Tang
Quality information on online platforms is often conveyed through simple, percentile-based badges and tiers that remain stable across different market environments. Motivated by th…
Reinforcement Learning in MDPs with Information-Ordered Policies
Zhongjun Zhang, Shipra Agrawal, Ilan Lobel +2
Many operational Markov decision processes provide structured information where a trajectory generated under one policy may also reveal how other policies would have performed. We…
Q-learning with Posterior Sampling
Priyank Agrawal, Shipra Agrawal, Azmat Azati
Bayesian posterior sampling techniques have demonstrated superior empirical performance in many exploration-exploitation settings. However, their theoretical analysis remains a cha…
Optimistic Q-learning for average reward and episodic reinforcement learning
Priyank Agrawal, Shipra Agrawal
We present an optimistic Q-learning algorithm for regret minimization in average reward reinforcement learning under an additional assumption on the underlying MDP that for all pol…
Dynamic Pricing and Learning with Long-term Reference Effects
Shipra Agrawal, Wei Tang
We consider a dynamic pricing problem where customer response to the current price is impacted by the customer price expectation, aka reference price. We study a simple and novel r…
Posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal, Randy Jia
We present an algorithm based on posterior sampling (aka Thompson sampling) that achieves near-optimal worst-case regret bounds when the underlying Markov Decision Process (MDP) is…