b'Kaiqing Zhang'

research

∙ 04/27/2023

Learning to Extrapolate: A Transductive Approach

Machine learning systems, especially with overparameterized deep neural ...

0 Aviv Netanyahu, et al. ∙

research

∙ 03/03/2023

A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games

We study two-player zero-sum stochastic games, and propose a form of ind...

0 Zaiwei Chen, et al. ∙

research

∙ 02/07/2023

Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation

We propose a new model, independent linear Markov game, for multi-agent ...

0 Qiwen Cui, et al. ∙

research

∙ 12/30/2022

Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?

We study the task of learning state representations from potentially hig...

0 Yi Tian, et al. ∙

research

∙ 12/28/2022

Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation

Offline reinforcement learning (RL) concerns pursuing an optimal policy ...

0 Asuman Ozdaglar, et al. ∙

research

∙ 11/15/2022

An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods

In this paper, we revisit and improve the convergence of policy gradient...

0 Yanli Liu, et al. ∙

research

∙ 10/23/2022

Symmetric (Optimistic) Natural Policy Gradient for Multi-agent Learning with Parameter Convergence

Multi-agent interactions are increasingly important in the context of re...

0 Sarath Pattathil, et al. ∙

research

∙ 10/20/2022

Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?

Decentralized learning has been advocated and widely deployed to make ef...

0 Lirui Wang, et al. ∙

research

∙ 10/10/2022

Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies

Gradient-based methods have been widely used for system design and optim...

0 Bin Hu, et al. ∙

research

∙ 06/19/2022

The Power of Regularization in Solving Extensive-Form Games

In this paper, we investigate the power of regularization, a common tech...

0 Mingyang Liu, et al. ∙

research

∙ 06/09/2022

What is a Good Metric to Study Generalization of Minimax Learners?

Minimax optimization has served as the backbone of many machine learning...

0 Asuman Ozdaglar, et al. ∙

research

∙ 06/06/2022

Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs

We study sequential decision making problems aimed at maximizing the exp...

0 Dongsheng Ding, et al. ∙

research

∙ 06/01/2022

Byzantine-Robust Online and Offline Distributed Reinforcement Learning

We consider a distributed reinforcement learning setting where multiple ...

0 Yiding Chen, et al. ∙

research

∙ 05/23/2022

Fictitious Play in Markov Games with Single Controller

Certain but important classes of strategic-form games, including zero-su...

0 Muhammed O. Sayin, et al. ∙

research

∙ 04/08/2022

The Complexity of Markov Equilibrium in Stochastic Games

We show that computing approximate stationary Markov coarse correlated e...

0 Constantinos Daskalakis, et al. ∙

research

∙ 02/23/2022

Globally Convergent Policy Search over Dynamic Filters for Output Estimation

We introduce the first direct policy search algorithm which provably con...

0 Jack Umenberger, et al. ∙

research

∙ 02/08/2022

Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence

We examine global non-asymptotic convergence properties of policy gradie...

0 Dongsheng Ding, et al. ∙

research

∙ 02/02/2022

Do Differentiable Simulators Give Better Policy Gradients?

Differentiable simulators promise faster computation time for reinforcem...

0 H. J. Terry Suh, et al. ∙

research

∙ 11/23/2021

Independent Learning in Stochastic Games

Reinforcement learning (RL) has recently achieved tremendous successes i...

0 Asuman Ozdaglar, et al. ∙

research

∙ 10/12/2021

On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) algorithms often suffer from a...

0 Weichao Mao, et al. ∙

research

∙ 06/04/2021

Decentralized Q-Learning in Zero-sum Markov Games

We study multi-agent reinforcement learning (MARL) in infinite-horizon d...

0 Muhammed O. Sayin, et al. ∙

research

∙ 01/14/2021

Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates

We study the multi-agent safe control problem where agents should avoid ...

0 Zengyi Qin, et al. ∙

research

∙ 01/04/2021

Derivative-Free Policy Optimization for Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

Direct policy search serves as one of the workhorses in modern reinforce...

0 Kaiqing Zhang, et al. ∙

research

∙ 12/31/2020

Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup

Asynchronous and parallel implementation of standard reinforcement learn...

3 Han Shen, et al. ∙

research

∙ 10/07/2020

Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs

We consider model-free reinforcement learning (RL) in non-stationary Mar...

2 Weichao Mao, et al. ∙

research

∙ 09/09/2020

Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games

In this paper, we study large population multi-agent reinforcement learn...

0 Muhammad Aneeq uz Zaman, et al. ∙

research

∙ 07/15/2020

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

Model-based reinforcement learning (RL), which finds an optimal policy u...

27 Kaiqing Zhang, et al. ∙

research

∙ 06/08/2020

POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis

Monte-Carlo planning, as exemplified by Monte-Carlo Tree Search (MCTS), ...

0 Weichao Mao, et al. ∙

research

∙ 04/02/2020

Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) under partial observability ha...

0 Weichao Mao, et al. ∙

research

∙ 03/30/2020

Approximate Equilibrium Computation for Discrete-Time Linear-Quadratic Mean-Field Games

While the topic of mean-field games (MFGs) has a relatively long history...

0 Muhammad Aneeq uz Zaman, et al. ∙

research

∙ 03/01/2020

Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks

This paper proposes a fully asynchronous scheme for policy evaluation of...

0 Xingyu Sha, et al. ∙

research

∙ 12/09/2019

Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances

Multi-agent reinforcement learning (MARL) has long been a significant an...

0 Kaiqing Zhang, et al. ∙

research

∙ 11/24/2019

Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms

Recent years have witnessed significant advances in reinforcement learni...

0 Kaiqing Zhang, et al. ∙

research

∙ 11/03/2019

Non-Cooperative Inverse Reinforcement Learning

Making decisions in the presence of a strategic opponent requires one to...

0 Xiangyuan Zhang, et al. ∙

research

∙ 10/21/2019

Policy Optimization for H_2 Linear Control with H_∞ Robustness Guarantee: Implicit Regularization and Global Convergence

Policy optimization (PO) is a key ingredient for reinforcement learning ...

0 Kaiqing Zhang, et al. ∙

research

∙ 08/06/2019

Online Planning for Decentralized Stochastic Control with Partial History Sharing

In decentralized stochastic control, standard approaches for sequential ...

0 Kaiqing Zhang, et al. ∙

research

∙ 07/13/2019

Stochastic Convergence Results for Regularized Actor-Critic Methods

In this paper, we present a stochastic convergence proof, under suitable...

0 Wesley Suttle, et al. ∙

research

∙ 07/06/2019

A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning

This paper considers a distributed reinforcement learning problem in whi...

0 Yixuan Lin, et al. ∙

research

∙ 06/19/2019

Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies

Policy gradient (PG) methods are a widely used reinforcement learning me...

0 Kaiqing Zhang, et al. ∙

research

∙ 05/31/2019

Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games

We study the global convergence of policy optimization for finding the N...

0 Kaiqing Zhang, et al. ∙

research

∙ 03/15/2019

A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning

This paper extends off-policy reinforcement learning to the multi-agent ...

0 Wesley Suttle, et al. ∙

research

∙ 12/07/2018

Communication-Efficient Distributed Reinforcement Learning

This paper studies the distributed reinforcement learning (DRL) problem ...

0 Tianyi Chen, et al. ∙

research

∙ 12/06/2018

Finite-Sample Analyses for Fully Decentralized Multi-Agent Reinforcement Learning

Despite the increasing interest in multi-agent reinforcement learning (M...

2 Kaiqing Zhang, et al. ∙

research

∙ 11/19/2018

Distributed Learning of Average Belief Over Networks Using Sequential Observations

This paper addresses the problem of distributed learning of average beli...

0 Kaiqing Zhang, et al. ∙

research

∙ 02/23/2018

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

We consider the problem of fully decentralized multi-agent reinforcement...

0 Kaiqing Zhang, et al. ∙

Kaiqing Zhang

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