Zhaoran Wang

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Princeton University ∙ Georgia Institute of Technology ∙ Johns Hopkins University

Graduate student in the Department of Operations Research and Financial Engineering at Princeton University

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

Designing off-policy reinforcement learning algorithms is typically a ve...
02/23/2021 ∙ by Tengyu Xu, et al. ∙ 0 ∙ share

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Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

In offline reinforcement learning (RL) an optimal policy is learnt solel...
02/19/2021 ∙ by Luofeng Liao, et al. ∙ 1 ∙ share

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

This paper proposes a new algorithm – the Momentum-assisted Single-times...
02/15/2021 ∙ by Prashant Khanduri, et al. ∙ 0 ∙ share

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Provably Training Neural Network Classifiers under Fairness Constraints

Training a classifier under fairness constraints has gotten increasing a...
12/30/2020 ∙ by You-Lin Chen, et al. ∙ 0 ∙ share

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Is Pessimism Provably Efficient for Offline RL?

We study offline reinforcement learning (RL), which aims to learn an opt...
12/30/2020 ∙ by Ying Jin, et al. ∙ 0 ∙ share

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

While deep reinforcement learning has achieved tremendous successes in v...
12/28/2020 ∙ by Han Zhong, et al. ∙ 0 ∙ share

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Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization

We consider the optimization problem of minimizing a functional defined ...
12/21/2020 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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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...
11/09/2020 ∙ by Zhuoran Yang, et al. ∙ 9 ∙ share

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End-to-End Learning and Intervention in Games

In a social system, the self-interest of agents can be detrimental to th...
10/26/2020 ∙ by Jiayang Li, et al. ∙ 0 ∙ share

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Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning

Efficient exploration remains a challenging problem in reinforcement lea...
10/17/2020 ∙ by Chenjia Bai, et al. ∙ 9 ∙ share

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Provable Fictitious Play for General Mean-Field Games

We propose a reinforcement learning algorithm for stationary mean-field ...
10/08/2020 ∙ by Qiaomin Xie, et al. ∙ 0 ∙ share

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Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection

We consider the stochastic contextual bandit problem under the high dime...
09/04/2020 ∙ by Yining Wang, et al. ∙ 5 ∙ share

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

Temporal-Difference (TD) learning with nonlinear smooth function approxi...
08/23/2020 ∙ by Shuang Qiu, et al. ∙ 0 ∙ share

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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...
08/16/2020 ∙ by Weichen Wang, et al. ∙ 0 ∙ share

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Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy

We study the global convergence and global optimality of actor-critic, o...
08/02/2020 ∙ by Zuyue Fu, et al. ∙ 0 ∙ share

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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 ...
07/10/2020 ∙ by Mingyi Hong, et al. ∙ 0 ∙ share

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Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion

Langevin diffusion is a powerful method for nonconvex optimization, whic...
07/04/2020 ∙ by Yi Chen, et al. ∙ 0 ∙ share

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

Structural equation models (SEMs) are widely used in sciences, ranging f...
07/02/2020 ∙ by Luofeng Liao, et al. ∙ 13 ∙ share

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Dynamic Regret of Policy Optimization in Non-stationary Environments

We consider reinforcement learning (RL) in episodic MDPs with adversaria...
06/30/2020 ∙ by Yingjie Fei, et al. ∙ 0 ∙ share

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On the Global Optimality of Model-Agnostic Meta-Learning

Model-agnostic meta-learning (MAML) formulates meta-learning as a bileve...
06/23/2020 ∙ by Lingxiao Wang, et al. ∙ 0 ∙ share

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

We study risk-sensitive reinforcement learning in episodic Markov decisi...
06/22/2020 ∙ by Yingjie Fei, et al. ∙ 0 ∙ share

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Provably Efficient Causal Reinforcement Learning with Confounded Observational Data

Empowered by expressive function approximators such as neural networks, ...
06/22/2020 ∙ by Lingxiao Wang, et al. ∙ 0 ∙ share

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

Multi-agent reinforcement learning (MARL) achieves significant empirical...
06/21/2020 ∙ by Lingxiao Wang, et al. ∙ 0 ∙ share

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Neural Certificates for Safe Control Policies

This paper develops an approach to learn a policy of a dynamical system ...
06/15/2020 ∙ by Wanxin Jin, et al. ∙ 0 ∙ share

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

Temporal-difference and Q-learning play a key role in deep reinforcement...
06/08/2020 ∙ by Yufeng Zhang, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning with Smooth Policy

Deep neural networks have been widely adopted in modern reinforcement le...
03/21/2020 ∙ by Qianli Shen, et al. ∙ 54 ∙ share

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

Generative adversarial imitation learning (GAIL) demonstrates tremendous...
03/08/2020 ∙ by Yufeng Zhang, et al. ∙ 0 ∙ share

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Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees

Graph representation learning is a ubiquitous task in machine learning w...
03/02/2020 ∙ by Sen Na, et al. ∙ 13 ∙ share

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Upper Confidence Primal-Dual Optimization: Stochastically Constrained Markov Decision Processes with Adversarial Losses and Unknown Transitions

We consider online learning for episodic Markov decision processes (MDPs...
03/02/2020 ∙ by Shuang Qiu, et al. ∙ 0 ∙ share

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Provably Efficient Safe Exploration via Primal-Dual Policy Optimization

We study the Safe Reinforcement Learning (SRL) problem using the Constra...
03/01/2020 ∙ by Dongsheng Ding, et al. ∙ 0 ∙ share

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Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium

We develop provably efficient reinforcement learning algorithms for two-...
02/17/2020 ∙ by Qiaomin Xie, et al. ∙ 0 ∙ share

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On Computation and Generalization of Generative Adversarial Imitation Learning

Generative Adversarial Imitation Learning (GAIL) is a powerful and pract...
01/09/2020 ∙ by Minshuo Chen, et al. ∙ 15 ∙ share

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Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework

This paper develops a Pontryagin differentiable programming (PDP) method...
12/30/2019 ∙ by Wanxin Jin, et al. ∙ 0 ∙ share

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Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator

Multi-agent reinforcement learning has been successfully applied to a nu...
12/14/2019 ∙ by Yuwei Luo, et al. ∙ 7 ∙ share

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Provably Efficient Exploration in Policy Optimization

While policy-based reinforcement learning (RL) achieves tremendous succe...
12/12/2019 ∙ by Qi Cai, et al. ∙ 0 ∙ share

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Convergent Policy Optimization for Safe Reinforcement Learning

We study the safe reinforcement learning problem with nonlinear function...
10/26/2019 ∙ by Ming Yu, et al. ∙ 29 ∙ share

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Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games

We study discrete-time mean-field Markov games with infinite numbers of ...
10/16/2019 ∙ by Zuyue Fu, et al. ∙ 0 ∙ share

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Credible Sample Elicitation by Deep Learning, for Deep Learning

It is important to collect credible training samples (x,y) for building ...
10/08/2019 ∙ by Yang Liu, et al. ∙ 0 ∙ share

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Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Policy gradient methods with actor-critic schemes demonstrate tremendous...
08/29/2019 ∙ by Lingxiao Wang, et al. ∙ 0 ∙ share

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Fast multi-agent temporal-difference learning via homotopy stochastic primal-dual optimization

We consider a distributed multi-agent policy evaluation problem in reinf...
08/07/2019 ∙ by Dongsheng Ding, et al. ∙ 0 ∙ share

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More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning

We consider the weakly supervised binary classification problem where th...
07/14/2019 ∙ by Xinyang Yi, et al. ∙ 6 ∙ share

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On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost

Despite the empirical success of the actor-critic algorithm, its theoret...
07/14/2019 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Provably Efficient Reinforcement Learning with Linear Function Approximation

Modern Reinforcement Learning (RL) is commonly applied to practical prob...
07/11/2019 ∙ by Chi Jin, et al. ∙ 3 ∙ share

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

This paper considers a distributed reinforcement learning problem in whi...
07/06/2019 ∙ by Yixuan Lin, et al. ∙ 0 ∙ share

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Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy

Proximal policy optimization and trust region policy optimization (PPO a...
06/25/2019 ∙ by Boyi Liu, et al. ∙ 0 ∙ share

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Neural Temporal-Difference Learning Converges to Global Optima

Temporal-difference learning (TD), coupled with neural networks, is amon...
05/24/2019 ∙ by Qi Cai, et al. ∙ 0 ∙ share

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

This paper extends off-policy reinforcement learning to the multi-agent ...
03/15/2019 ∙ by Wesley Suttle, et al. ∙ 0 ∙ share

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On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator

We study the global convergence of generative adversarial imitation lear...
01/11/2019 ∙ by Qi Cai, et al. ∙ 0 ∙ share

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A Theoretical Analysis of Deep Q-Learning

Despite the great empirical success of deep reinforcement learning, its ...
01/01/2019 ∙ by Zhuora Yang, et al. ∙ 0 ∙ share

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Provable Gaussian Embedding with One Observation

The success of machine learning methods heavily relies on having an appr...
10/25/2018 ∙ by Ming Yu, et al. ∙ 0 ∙ share

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Zhaoran Wang

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