b'Zhuoran Yang'

research

∙ 06/03/2022

Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games

We study decentralized policy learning in Markov games where we control ...

Wenhao Zhan, et al. ∙

research

∙ 05/26/2022

Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes

We study offline reinforcement learning (RL) in partially observable Mar...

Miao Lu, et al. ∙

research

∙ 05/26/2022

Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency

Reinforcement learning in partially observed Markov decision processes (...

Lingxiao Wang, et al. ∙

research

∙ 05/23/2022

Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation

We study human-in-the-loop reinforcement learning (RL) with trajectory p...

Xiaoyu Chen, et al. ∙

research

∙ 05/05/2022

Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning

Dynamic mechanism design has garnered significant attention from both co...

Boxiang Lyu, et al. ∙

research

∙ 04/20/2022

Sample-Efficient Reinforcement Learning for POMDPs with Linear Function Approximations

Despite the success of reinforcement learning (RL) for Markov decision p...

Qi Cai, et al. ∙

research

∙ 03/07/2022

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

We study a Markov matching market involving a planner and a set of strat...

Yifei Min, et al. ∙

research

∙ 03/03/2022

The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches

In this paper, we study the problem of regret minimization for episodic ...

Grigoris Velegkas, et al. ∙

research

∙ 02/25/2022

Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach

Dynamic mechanism design studies how mechanism designers should allocate...

Boxiang Lyu, et al. ∙

research

∙ 02/23/2022

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

Offline Reinforcement Learning (RL) aims to learn policies from previous...

Chenjia Bai, et al. ∙

research

∙ 02/22/2022

Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning

In today's economy, it becomes important for Internet platforms to consi...

Jibang Wu, et al. ∙

research

∙ 01/28/2022

Joint Differentiable Optimization and Verification for Certified Reinforcement Learning

In model-based reinforcement learning for safety-critical control system...

Yixuan Wang, et al. ∙

research

∙ 12/28/2021

Exponential Family Model-Based Reinforcement Learning via Score Matching

We propose an optimistic model-based algorithm, dubbed SMRL, for finite-...

Gene Li, et al. ∙

research

∙ 12/27/2021

Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic

Actor-critic (AC) algorithms, empowered by neural networks, have had sig...

Yufeng Zhang, et al. ∙

research

∙ 12/27/2021

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?

We study multi-player general-sum Markov games with one of the players d...

Han Zhong, et al. ∙

research

∙ 11/06/2021

Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning

We study risk-sensitive reinforcement learning (RL) based on the entropi...

Yingjie Fei, et al. ∙

research

∙ 10/24/2021

SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn the optimal policy fro...

Zhihong Deng, et al. ∙

research

∙ 10/19/2021

On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

To achieve sample efficiency in reinforcement learning (RL), it necessit...

Shuang Qiu, et al. ∙

research

∙ 10/18/2021

Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs

We study episodic reinforcement learning (RL) in non-stationary linear k...

Han Zhong, et al. ∙

research

∙ 10/04/2021

Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima

To regulate a social system comprised of self-interested agents, economi...

Boyi Liu, et al. ∙

research

∙ 08/19/2021

Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation

In generative adversarial imitation learning (GAIL), the agent aims to l...

Zhihan Liu, et al. ∙

research

∙ 08/08/2021

Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning

The recent emergence of reinforcement learning has created a demand for ...

Pratik Ramprasad, et al. ∙

research

∙ 07/30/2021

Towards General Function Approximation in Zero-Sum Markov Games

This paper considers two-player zero-sum finite-horizon Markov games wit...

Baihe Huang, et al. ∙

research

∙ 07/06/2021

A Unified Off-Policy Evaluation Approach for General Value Function

General Value Function (GVF) is a powerful tool to represent both the pr...

Tengyu Xu, et al. ∙

research

∙ 07/01/2021

Gap-Dependent Bounds for Two-Player Markov Games

As one of the most popular methods in the field of reinforcement learnin...

Zehao Dou, et al. ∙

research

∙ 06/15/2021

Randomized Exploration for Reinforcement Learning with General Value Function Approximation

We propose a model-free reinforcement learning algorithm inspired by the...

Haque Ishfaq, et al. ∙

research

∙ 02/23/2021

Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality

Designing off-policy reinforcement learning algorithms is typically a ve...

Tengyu Xu, et al. ∙

research

∙ 02/19/2021

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

In offline reinforcement learning (RL) an optimal policy is learnt solel...

Luofeng Liao, et al. ∙

research

∙ 02/15/2021

A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization

This paper proposes a new algorithm – the Momentum-assisted Single-times...

Prashant Khanduri, et al. ∙

research

∙ 12/30/2020

Is Pessimism Provably Efficient for Offline RL?

We study offline reinforcement learning (RL), which aims to learn an opt...

Ying Jin, et al. ∙

research

∙ 12/28/2020

Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy

While deep reinforcement learning has achieved tremendous successes in v...

Han Zhong, et al. ∙

research

∙ 12/21/2020

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization

We consider the optimization problem of minimizing a functional defined ...

Zhuoran Yang, et al. ∙

research

∙ 11/09/2020

On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces

The classical theory of reinforcement learning (RL) has focused on tabul...

Zhuoran Yang, et al. ∙

research

∙ 10/08/2020

Provable Fictitious Play for General Mean-Field Games

We propose a reinforcement learning algorithm for stationary mean-field ...

Qiaomin Xie, et al. ∙

research

∙ 08/23/2020

Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning

Temporal-Difference (TD) learning with nonlinear smooth function approxi...

Shuang Qiu, et al. ∙

research

∙ 08/16/2020

Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time

Reinforcement learning is a powerful tool to learn the optimal policy of...

Weichen Wang, et al. ∙

research

∙ 08/02/2020

Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy

We study the global convergence and global optimality of actor-critic, o...

Zuyue Fu, et al. ∙

research

∙ 07/16/2020

Understanding Implicit Regularization in Over-Parameterized Nonlinear Statistical Model

We study the implicit regularization phenomenon induced by simple optimi...

Jianqing Fan, et al. ∙

research

∙ 07/10/2020

A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic

This paper analyzes a two-timescale stochastic algorithm for a class of ...

Mingyi Hong, et al. ∙

research

∙ 07/02/2020

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach

Structural equation models (SEMs) are widely used in sciences, ranging f...

Luofeng Liao, et al. ∙

research

∙ 06/30/2020

Dynamic Regret of Policy Optimization in Non-stationary Environments

We consider reinforcement learning (RL) in episodic MDPs with adversaria...

Yingjie Fei, et al. ∙

research

∙ 06/23/2020

On the Global Optimality of Model-Agnostic Meta-Learning

Model-agnostic meta-learning (MAML) formulates meta-learning as a bileve...

Lingxiao Wang, et al. ∙

research

∙ 06/22/2020

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

We study risk-sensitive reinforcement learning in episodic Markov decisi...

Yingjie Fei, et al. ∙

research

∙ 06/22/2020

Provably Efficient Causal Reinforcement Learning with Confounded Observational Data

Empowered by expressive function approximators such as neural networks, ...

Lingxiao Wang, et al. ∙

research

∙ 06/21/2020

Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning

Multi-agent reinforcement learning (MARL) achieves significant empirical...

Lingxiao Wang, et al. ∙

research

∙ 06/15/2020

Neural Certificates for Safe Control Policies

This paper develops an approach to learn a policy of a dynamical system ...

Wanxin Jin, et al. ∙

research

∙ 06/08/2020

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory

Temporal-difference and Q-learning play a key role in deep reinforcement...

Yufeng Zhang, et al. ∙

research

∙ 03/09/2020

An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data

In medical studies, the collected covariates usually contain underlying ...

Liya Fu, et al. ∙

research

∙ 03/08/2020

Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate

Generative adversarial imitation learning (GAIL) demonstrates tremendous...

Yufeng Zhang, et al. ∙

research

∙ 03/02/2020

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees

Graph representation learning is a ubiquitous task in machine learning w...

Sen Na, et al. ∙

Zhuoran Yang

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