6 papers
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…
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
Yi Tian, Kaiqing Zhang, Russ Tedrake +1
We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a…
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs
Dongsheng Ding, Kaiqing Zhang, Jiali Duan +2
We study the sequential decision making problem of maximizing the expected total reward while satisfying a constraint on the expected total utility. We employ the natural policy gr…
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
Chanwoo Park, Kaiqing Zhang, Asuman Ozdaglar
We study a new class of Markov games, \emph(multi-player) zero-sum Markov Games} with \emph{Networked separable interactions} (zero-sum NMGs), to model the local interaction struct…
The Power of Regularization in Solving Extensive-Form Games
Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu +1
In this paper, we investigate the power of {\it regularization}, a common technique in reinforcement learning and optimization, in solving extensive-form games (EFGs). We propose a…
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints
Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang +1
Offline reinforcement learning (RL) aims to find an optimal policy for Markov decision processes (MDPs) using a pre-collected dataset. In this work, we revisit the linear programmi…