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On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift

1 August 2019

Papers citing "On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift"

22 / 222 papers shown

Title
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning Alireza Fallah Kristian Georgiev Aryan Mokhtari Asuman Ozdaglar 136 23 0 12 Feb 2020
Statistically Efficient Off-Policy Policy Gradients Nathan Kallus Masatoshi Uehara OffRL 120 39 0 10 Feb 2020
Reward-Free Exploration for Reinforcement Learning Chi Jin A. Krishnamurthy Max Simchowitz Tiancheng Yu OffRL 190 197 0 07 Feb 2020
Provably Efficient Reinforcement Learning with Aggregated States Shi Dong Benjamin Van Roy Zhengyuan Zhou 59 32 0 13 Dec 2019
Provably Efficient Exploration in Policy Optimization Qi Cai Zhuoran Yang Chi Jin Zhaoran Wang 120 283 0 12 Dec 2019
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation Pan Xu Quanquan Gu 90 68 0 10 Dec 2019
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms Jianchao Tan Zhuoran Yang Tamer Basar 285 1,233 0 24 Nov 2019
Safe Policies for Reinforcement Learning via Primal-Dual Methods Santiago Paternain Miguel Calvo-Fullana Luiz F. O. Chamon Alejandro Ribeiro 104 105 0 20 Nov 2019
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning Dipendra Kumar Misra Mikael Henaff A. Krishnamurthy John Langford 106 151 0 13 Nov 2019
$Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence$ Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence Jianchao Tan Bin Hu Tamer Basar 97 121 0 21 Oct 2019
On Connections between Constrained Optimization and Reinforcement Learning Nino Vieillard Olivier Pietquin Matthieu Geist 52 13 0 18 Oct 2019
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes Chen-Yu Wei Mehdi Jafarnia-Jahromi Haipeng Luo Hiteshi Sharma R. Jain 182 108 0 15 Oct 2019
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? S. Du Sham Kakade Ruosong Wang Lin F. Yang 260 193 0 07 Oct 2019
Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs Lior Shani Yonathan Efroni Shie Mannor 106 176 0 06 Sep 2019
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence Lingxiao Wang Qi Cai Zhuoran Yang Zhaoran Wang 121 242 0 29 Aug 2019
A Review of Cooperative Multi-Agent Deep Reinforcement Learning Afshin Oroojlooyjadid Davood Hajinezhad 126 439 0 11 Aug 2019
Policy Optimization with Stochastic Mirror Descent Long Yang Yu Zhang Gang Zheng Qian Zheng Pengfei Li Jianhang Huang Jun Wen Gang Pan 133 34 0 25 Jun 2019
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy Boyi Liu Qi Cai Zhuoran Yang Zhaoran Wang 118 111 0 25 Jun 2019
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies Jianchao Tan Alec Koppel Haoqi Zhu Tamer Basar 122 191 0 19 Jun 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 140 193 0 05 Jun 2019
Policy Search by Target Distribution Learning for Continuous Control Wei Shen Yuanqi Li Jian Li 75 6 0 27 May 2019
Smoothing Policies and Safe Policy Gradients Matteo Papini Matteo Pirotta Marcello Restelli 80 31 0 08 May 2019