Zhuoran Yang

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Princeton University

Research Internt at Princeton University

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. ∙ 28 ∙ 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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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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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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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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Finite-Sample Analyses for Fully Decentralized Multi-Agent Reinforcement Learning

Despite the increasing interest in multi-agent reinforcement learning (M...
12/06/2018 ∙ by Kaiqing Zhang, et al. ∙ 2 ∙ share

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Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rates and Global Landscape Analysis

We study the robust one-bit compressed sensing problem whose goal is to ...
08/14/2019 ∙ by Shuang Qiu, et al. ∙ 2 ∙ share

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On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models

We consider estimating the parametric components of semi-parametric mult...
09/26/2017 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference

We study parameter estimation and asymptotic inference for sparse nonlin...
11/14/2015 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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On Semiparametric Exponential Family Graphical Models

We propose a new class of semiparametric exponential family graphical mo...
12/30/2014 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Misspecified Nonconvex Statistical Optimization for Phase Retrieval

Existing nonconvex statistical optimization theory and methods crucially...
12/18/2017 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

We consider the problem of fully decentralized multi-agent reinforcement...
02/23/2018 ∙ by Kaiqing Zhang, et al. ∙ 0 ∙ share

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Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

Despite the success of single-agent reinforcement learning, multi-agent ...
06/03/2018 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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Tensor Methods for Additive Index Models under Discordance and Heterogeneity

Motivated by the sampling problems and heterogeneity issues common in hi...
07/17/2018 ∙ by Krishnakumar Balasubramanian, et al. ∙ 0 ∙ share

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High-dimensional Varying Index Coefficient Models via Stein's Identity

We study the parameter estimation problem for a single-index varying coe...
10/16/2018 ∙ by Sen Na, et al. ∙ 0 ∙ share

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Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

Most existing deep reinforcement learning (DRL) frameworks consider eith...
10/10/2018 ∙ by Jiechao Xiong, 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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Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval

We study the fundamental tradeoffs between statistical accuracy and comp...
08/21/2018 ∙ by Jianqing Fan, 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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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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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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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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Stochastic Convergence Results for Regularized Actor-Critic Methods

In this paper, we present a stochastic convergence proof, under suitable...
07/13/2019 ∙ by Wesley Suttle, 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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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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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...
05/31/2019 ∙ by Kaiqing Zhang, et al. ∙ 0 ∙ 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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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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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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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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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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Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances

Multi-agent reinforcement learning (MARL) has long been a significant an...
12/09/2019 ∙ by Kaiqing Zhang, et al. ∙ 0 ∙ share

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

Recent years have witnessed significant advances in reinforcement learni...
11/24/2019 ∙ by Kaiqing Zhang, et al. ∙ 0 ∙ 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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Zhuoran Yang

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