
Convergent Policy Optimization for Safe Reinforcement Learning
We study the safe reinforcement learning problem with nonlinear function...
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On Computation and Generalization of Generative Adversarial Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a powerful and pract...
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Natural ActorCritic Converges Globally for Hierarchical Linear Quadratic Regulator
Multiagent reinforcement learning has been successfully applied to a nu...
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More Supervision, Less Computation: StatisticalComputational Tradeoffs in Weakly Supervised Learning
We consider the weakly supervised binary classification problem where th...
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Provably Efficient Reinforcement Learning with Linear Function Approximation
Modern Reinforcement Learning (RL) is commonly applied to practical prob...
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FiniteSample Analyses for Fully Decentralized MultiAgent Reinforcement Learning
Despite the increasing interest in multiagent reinforcement learning (M...
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Robust OneBit Recovery via ReLU Generative Networks: Improved Statistical Rates and Global Landscape Analysis
We study the robust onebit compressed sensing problem whose goal is to ...
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On Stein's Identity and NearOptimal Estimation in Highdimensional Index Models
We consider estimating the parametric components of semiparametric mult...
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Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
We study parameter estimation and asymptotic inference for sparse nonlin...
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On Semiparametric Exponential Family Graphical Models
We propose a new class of semiparametric exponential family graphical mo...
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Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Existing nonconvex statistical optimization theory and methods crucially...
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Fully Decentralized MultiAgent Reinforcement Learning with Networked Agents
We consider the problem of fully decentralized multiagent reinforcement...
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MultiAgent Reinforcement Learning via Double Averaging PrimalDual Optimization
Despite the success of singleagent reinforcement learning, multiagent ...
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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...
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Highdimensional Varying Index Coefficient Models via Stein's Identity
We study the parameter estimation problem for a singleindex varying coe...
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Parametrized Deep QNetworks Learning: Reinforcement Learning with DiscreteContinuous Hybrid Action Space
Most existing deep reinforcement learning (DRL) frameworks consider eith...
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Provable Gaussian Embedding with One Observation
The success of machine learning methods heavily relies on having an appr...
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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...
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A MultiAgent OffPolicy ActorCritic Algorithm for Distributed Reinforcement Learning
This paper extends offpolicy reinforcement learning to the multiagent ...
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Neural TemporalDifference Learning Converges to Global Optima
Temporaldifference learning (TD), coupled with neural networks, is amon...
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Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy
Proximal policy optimization and trust region policy optimization (PPO a...
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On the Global Convergence of ActorCritic: A Case for Linear Quadratic Regulator with Ergodic Cost
Despite the empirical success of the actorcritic algorithm, its theoret...
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Stochastic Convergence Results for Regularized ActorCritic Methods
In this paper, we present a stochastic convergence proof, under suitable...
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Fast multiagent temporaldifference learning via homotopy stochastic primaldual optimization
We consider a distributed multiagent policy evaluation problem in reinf...
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Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Policy gradient methods with actorcritic schemes demonstrate tremendous...
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Policy Optimization Provably Converges to Nash Equilibria in ZeroSum Linear Quadratic Games
We study the global convergence of policy optimization for finding the N...
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A CommunicationEfficient MultiAgent ActorCritic Algorithm for Distributed Reinforcement Learning
This paper considers a distributed reinforcement learning problem in whi...
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Provably Efficient Exploration in Policy Optimization
While policybased reinforcement learning (RL) achieves tremendous succe...
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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 ...
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Pontryagin Differentiable Programming: An EndtoEnd Learning and Control Framework
This paper develops a Pontryagin differentiable programming (PDP) method...
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Learning ZeroSum SimultaneousMove Markov Games Using Function Approximation and Correlated Equilibrium
We develop provably efficient reinforcement learning algorithms for two...
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Decentralized MultiAgent Reinforcement Learning with Networked Agents: Recent Advances
Multiagent reinforcement learning (MARL) has long been a significant an...
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MultiAgent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Recent years have witnessed significant advances in reinforcement learni...
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ActorCritic Provably Finds Nash Equilibria of LinearQuadratic MeanField Games
We study discretetime meanfield Markov games with infinite numbers of ...
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