69 citations · 365 across the 21 of their papers we have counts for
39 papers
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP
Jinghan Wang, Mengdi Wang, Lin F. Yang
This work considers the sample complexity of obtaining an -optimal policy in an average reward Markov Decision Process (AMDP), given access to a generative model (simu…
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally
Nived Rajaraman, Yanjun Han, Lin F. Yang +2
We study the statistical limits of Imitation Learning (IL) in episodic Markov Decision Processes (MDPs) with a state space . We focus on the known-transition setting w…
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost
Minbo Gao, Tianle Xie, Simon S. Du +1
Many real-world applications, such as those in medical domains, recommendation systems, etc, can be formulated as large state space reinforcement learning problems with only a smal…
Minimax Sample Complexity for Turn-based Stochastic Game
Qiwen Cui, Lin F. Yang
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 appr…
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
In this paper we consider multi-objective reinforcement learning where the objectives are balanced using preferences. In practice, the preferences are often given in an adversarial…
Episodic Linear Quadratic Regulators with Low-rank Transitions
Tianyu Wang, Lin F. Yang
Linear Quadratic Regulators (LQR) achieve enormous successful real-world applications. Very recently, people have been focusing on efficient learning algorithms for LQRs when their…