48 citations · 173 across the 14 of their papers we have counts for
15 papers · 1 filter
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing
Xiangyu Liu, Kaiqing Zhang
We study provable multi-agent reinforcement learning (RL) in the general framework of partially observable stochastic games (POSGs). To circumvent the known hardness results and th…
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective
Max Simchowitz, Abhishek Gupta, Kaiqing Zhang
Obtaining rigorous statistical guarantees for generalization under distribution shift remains an open and active research area. We study a setting we call combinatorial distributio…
Self-Supervised Reinforcement Learning that Transfers using Random Features
Boyuan Chen, Chuning Zhu, Pulkit Agrawal +2
Model-free reinforcement learning algorithms have exhibited great potential in solving single-task sequential decision-making problems with high-dimensional observations and long h…
Learning to Extrapolate: A Transductive Approach
Aviv Netanyahu, Abhishek Gupta, Max Simchowitz +2
Machine learning systems, especially with overparameterized deep neural networks, can generalize to novel test instances drawn from the same distribution as the training data. Howe…
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Yanli Liu, Kaiqing Zhang, Tamer Başar +1
In this paper, we revisit and improve the convergence of policy gradient (PG), natural PG (NPG) methods, and their variance-reduced variants, under general smooth policy parametriz…
The Complexity of Markov Equilibrium in Stochastic Games
Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang
We show that computing approximate stationary Markov coarse correlated equilibria (CCE) in general-sum stochastic games is computationally intractable, even when there are two play…