Lin F. Yang

Universal Streaming of Subset Norms

2018 · arXiv (Cornell University)

Most known algorithms in the streaming model of computation aim to approximate a single function such as an $\ell_p$-norm. In 2009, Nelson [\url{https://sublinear.info}, Open Problem 30] asked if it possible to design \emph{universal algorithms}, that …

Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?

2020 · arXiv (Cornell University)

Learning to plan for long horizons is a central challenge in episodic reinforcement learning problems. A fundamental question is to understand how the difficulty of the problem scales as the horizon increases. Here the natural …

Minimax Sample Complexity for Turn-based Stochastic Game

2021

The empirical success of multi-agent reinforcement learning is encouraging, while few theoretical guarantees have been revealed. In this work, we prove that the plug-in solver approach, probably the most natural reinforcement learning algorithm, achieves minimax …

Near-Optimal Sample Complexity Bounds for Constrained MDPs

2022 · arXiv (Cornell University)

In contrast to the advances in characterizing the sample complexity for solving Markov decision processes (MDPs), the optimal statistical complexity for solving constrained MDPs (CMDPs) remains unknown. We resolve this question by providing minimax upper …