5 citations · 29 across the 29 of their papers we have counts for
4 papers · 1 filter
Leveraging Offline Data in Online Reinforcement Learning
Andrew Wagenmaker, Aldo Pacchiano
Two central paradigms have emerged in the reinforcement learning (RL) community: online RL and offline RL. In the online RL setting, the agent has no prior knowledge of the environ…
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design
Andrew Wagenmaker, Kevin Jamieson
While much progress has been made in understanding the minimax sample complexity of reinforcement learning (RL) -- the complexity of learning on the "worst-case" instance -- such m…
Active Learning with Safety Constraints
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern +2
Active learning methods have shown great promise in reducing the number of samples necessary for learning. As automated learning systems are adopted into real-time, real-world deci…
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Andrew Wagenmaker, Yifang Chen, Max Simchowitz +2
Reward-free reinforcement learning (RL) considers the setting where the agent does not have access to a reward function during exploration, but must propose a near-optimal policy f…