2 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
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…