On the Sample Complexity of Reinforcement Learning with Policy Space Generalization

17 August 2020

Papers citing "On the Sample Complexity of Reinforcement Learning with Policy Space Generalization"

24 / 24 papers shown

Title
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity S. Du Jason D. Lee G. Mahajan Ruosong Wang 35 37 0 17 Feb 2020
Provably Efficient Exploration in Policy Optimization Qi Cai Zhuoran Yang Chi Jin Zhaoran Wang 39 278 0 12 Dec 2019
Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions J. Schmidhuber 33 128 0 05 Dec 2019
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? S. Du Sham Kakade Ruosong Wang Lin F. Yang 112 192 0 07 Oct 2019
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift Alekh Agarwal Sham Kakade Jason D. Lee G. Mahajan 44 320 0 01 Aug 2019
Worst-Case Regret Bounds for Exploration via Randomized Value Functions Daniel Russo OffRL 30 82 0 07 Jun 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 60 188 0 05 Jun 2019
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs Max Simchowitz Kevin Jamieson 52 144 0 09 May 2019
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems Dhruv Malik A. Pananjady Kush S. Bhatia K. Khamaru Peter L. Bartlett Martin J. Wainwright 45 198 0 20 Dec 2018
Information-Directed Exploration for Deep Reinforcement Learning Nikolay Nikolov Johannes Kirschner Felix Berkenkamp Andreas Krause 43 69 0 18 Dec 2018
Private PAC learning implies finite Littlestone dimension N. Alon Roi Livni M. Malliaris Shay Moran 38 109 0 04 Jun 2018
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator Maryam Fazel Rong Ge Sham Kakade M. Mesbahi 69 599 0 15 Jan 2018
Proximal Policy Optimization Algorithms John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov OffRL 241 18,685 0 20 Jul 2017
Noisy Networks for Exploration Meire Fortunato M. G. Azar Bilal Piot Jacob Menick Ian Osband ... Rémi Munos Demis Hassabis Olivier Pietquin Charles Blundell Shane Legg 68 890 0 30 Jun 2017
Deep Exploration via Randomized Value Functions Ian Osband Benjamin Van Roy Daniel Russo Zheng Wen 71 302 0 22 Mar 2017
Minimax Regret Bounds for Reinforcement Learning M. G. Azar Ian Osband Rémi Munos 68 771 0 16 Mar 2017
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable Nan Jiang A. Krishnamurthy Alekh Agarwal John Langford Robert Schapire 90 417 0 29 Oct 2016
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband Benjamin Van Roy BDL 74 257 0 01 Jul 2016
Continuous control with deep reinforcement learning Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel N. Heess Tom Erez Yuval Tassa David Silver Daan Wierstra 207 13,174 0 09 Sep 2015
End-to-End Training of Deep Visuomotor Policies Sergey Levine Chelsea Finn Trevor Darrell Pieter Abbeel BDL 235 3,418 0 02 Apr 2015
Trust Region Policy Optimization John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel 245 6,722 0 19 Feb 2015
Model-based Reinforcement Learning and the Eluder Dimension Ian Osband Benjamin Van Roy 65 188 0 07 Jun 2014
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization Zheng Wen Benjamin Van Roy 43 42 0 18 Jul 2013
Learning to Optimize Via Posterior Sampling Daniel Russo Benjamin Van Roy 137 699 0 11 Jan 2013