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08/16/2023
Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing
We study provable multi-agent reinforcement learning (MARL) in the gener...
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07/13/2023
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
We study a new class of Markov games (MGs), Multi-player Zero-sum Markov...
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07/12/2023
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective
Obtaining rigorous statistical guarantees for generalization under distr...
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06/20/2023
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
We study the problem of computing an optimal policy of an infinite-horiz...
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04/27/2023
Learning to Extrapolate: A Transductive Approach
Machine learning systems, especially with overparameterized deep neural ...
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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...
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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 ...
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12/30/2022
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?
We study the task of learning state representations from potentially hig...
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12/28/2022
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
Offline reinforcement learning (RL) concerns pursuing an optimal policy ...
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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...
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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...
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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...
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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...
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06/19/2022
The Power of Regularization in Solving Extensive-Form Games
In this paper, we investigate the power of regularization, a common tech...
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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...
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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...
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06/01/2022
Byzantine-Robust Online and Offline Distributed Reinforcement Learning
We consider a distributed reinforcement learning setting where multiple ...
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05/23/2022
Fictitious Play in Markov Games with Single Controller
Certain but important classes of strategic-form games, including zero-su...
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04/08/2022
The Complexity of Markov Equilibrium in Stochastic Games
We show that computing approximate stationary Markov coarse correlated e...
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02/23/2022
Globally Convergent Policy Search over Dynamic Filters for Output Estimation
We introduce the first direct policy search algorithm which provably con...
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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...
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02/02/2022
Do Differentiable Simulators Give Better Policy Gradients?
Differentiable simulators promise faster computation time for reinforcem...
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11/23/2021
Independent Learning in Stochastic Games
Reinforcement learning (RL) has recently achieved tremendous successes i...
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10/12/2021
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) algorithms often suffer from a...
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06/04/2021
Decentralized Q-Learning in Zero-sum Markov Games
We study multi-agent reinforcement learning (MARL) in infinite-horizon d...
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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 ...
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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...
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12/31/2020
Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup
Asynchronous and parallel implementation of standard reinforcement learn...
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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...
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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...
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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...
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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), ...
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04/02/2020
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) under partial observability ha...
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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...
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03/01/2020
Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks
This paper proposes a fully asynchronous scheme for policy evaluation of...
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12/09/2019
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances
Multi-agent reinforcement learning (MARL) has long been a significant an...
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11/24/2019
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Recent years have witnessed significant advances in reinforcement learni...
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11/03/2019
Non-Cooperative Inverse Reinforcement Learning
Making decisions in the presence of a strategic opponent requires one to...
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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 ...
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08/06/2019
Online Planning for Decentralized Stochastic Control with Partial History Sharing
In decentralized stochastic control, standard approaches for sequential ...
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07/13/2019
Stochastic Convergence Results for Regularized Actor-Critic Methods
In this paper, we present a stochastic convergence proof, under suitable...
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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...
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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...
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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...
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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 ...
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12/07/2018
Communication-Efficient Distributed Reinforcement Learning
This paper studies the distributed reinforcement learning (DRL) problem ...
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12/06/2018
Finite-Sample Analyses for Fully Decentralized Multi-Agent Reinforcement Learning
Despite the increasing interest in multi-agent reinforcement learning (M...
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11/19/2018
Distributed Learning of Average Belief Over Networks Using Sequential Observations
This paper addresses the problem of distributed learning of average beli...
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02/23/2018