25 citations · 38 across the 14 of their papers we have counts for
5 papers · 2 filters
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi +1
In the classical multi-armed bandit problem, instance-dependent algorithms attain improved performance on "easy" problems with a gap between the best and second-best arm. Are simil…
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control
Weichao Mao, Kaiqing Zhang, Ruihao Zhu +2
We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arb…
Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings
Xiao-Yue Gong, David Simchi-Levi
Motivated by the episodic version of the classical inventory control problem, we propose a new Q-learning-based algorithm, Elimination-Based Half-Q-Learning (HQL), that enjoys impr…
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i.e., both the reward and state transition distributions…
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability
David Simchi-Levi, Yunzong Xu
We consider the general (stochastic) contextual bandit problem under the realizability assumption, i.e., the expected reward, as a function of contexts and actions, belongs to a ge…