1 citations · 1 across the 10 of their papers we have counts for
13 papers
Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs
Tianhao Wu, Matthew Zurek, Weina Wang +1
We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction…
Optimality of a Threshold Policy for a Queueing System with One Fast Server and Two Identical Slow Servers
Weina Wang, Taha Ameen, Yudong Chen +3
This paper studies the optimal control problem of a queueing system with three servers: one fast server and two identical slow servers. The two-server version of this problem, with…
Optimal Variance-Dependent Regret Bounds for Infinite-Horizon MDPs
Guy Zamir, Matthew Zurek, Yudong Chen
Online reinforcement learning in infinite-horizon Markov decision processes (MDPs) remains less theoretically and algorithmically developed than its episodic counterpart, with many…
Faster Fixed-Point Methods for Multichain MDPs
Matthew Zurek, Yudong Chen
We study value-iteration (VI) algorithms for solving general (a.k.a. multichain) Markov decision processes (MDPs) under the average-reward criterion, a fundamental but theoreticall…
Optimal Single-Policy Sample Complexity and Transient Coverage for Average-Reward Offline RL
Matthew Zurek, Guy Zamir, Yudong Chen
We study offline reinforcement learning in average-reward MDPs, which presents increased challenges from the perspectives of distribution shift and non-uniform coverage, and has be…
Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for Average-Reward RL
Matthew Zurek, Yudong Chen
We study the sample complexity of finding an -optimal policy in average-reward Markov Decision Processes (MDPs) with a generative model. The minimax optimal span-based…
The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis
Matthew Zurek, Yudong Chen
We study the sample complexity of the plug-in approach for learning -optimal policies in average-reward Markov decision processes (MDPs) with a generative model. The p…
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
Matthew Zurek, Yudong Chen
We study the sample complexity of learning an -optimal policy in an average-reward Markov decision process (MDP) under a generative model. For weakly communicating MDP…
Span-Based Optimal Sample Complexity for Average Reward MDPs
Matthew Zurek, Yudong Chen
We study the sample complexity of learning an -optimal policy in an average-reward Markov decision process (MDP) under a generative model. We establish the complexity…
Gap-Free Clustering: Sensitivity and Robustness of SDP
Matthew Zurek, Yudong Chen
We study graph clustering in the Stochastic Block Model (SBM) in the presence of both large clusters and small, unrecoverable clusters. Previous convex relaxation approaches achiev…
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
Brahma S. Pavse, Matthew Zurek, Yudong Chen +2
In many reinforcement learning (RL) applications, we want policies that reach desired states and then keep the controlled system within an acceptable region around the desired stat…
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types
Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown +1
When inferring reward functions from human behavior (be it demonstrations, comparisons, physical corrections, or e-stops), it has proven useful to model the human as making noisy-r…
Situational Confidence Assistance for Lifelong Shared Autonomy
Matthew Zurek, Andreea Bobu, Daniel S. Brown +1
Shared autonomy enables robots to infer user intent and assist in accomplishing it. But when the user wants to do a new task that the robot does not know about, shared autonomy wil…