212 citations · 575 across the 20 of their papers we have counts for
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Batched Neural Bandits
Quanquan Gu, Amin Karbasi, Khashayar Khosravi +2
In many sequential decision-making problems, the individuals are split into several batches and the decision-maker is only allowed to change her policy at the end of batches. These…
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Yue Wu, Dongruo Zhou, Quanquan Gu
We study reinforcement learning in an infinite-horizon average-reward setting with linear function approximation, where the transition probability function of the underlying Markov…
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Dongruo Zhou, Quanquan Gu, Csaba Szepesvari
We study reinforcement learning (RL) with linear function approximation where the underlying transition probability kernel of the Markov decision process (MDP) is a linear mixture…