Deep Exploration via Randomized Value Functions

22 March 2017

Papers citing "Deep Exploration via Randomized Value Functions"

26 / 76 papers shown

Title
BeBold: Exploration Beyond the Boundary of Explored Regions Tianjun Zhang Huazhe Xu Xiaolong Wang Yi Wu Kurt Keutzer Joseph E. Gonzalez Yuandong Tian 28 40 0 15 Dec 2020
Policy Optimization as Online Learning with Mediator Feedback Alberto Maria Metelli Matteo Papini P. DÓro Marcello Restelli OffRL 19 10 0 15 Dec 2020
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling Ying Fan Yifei Ming 19 17 0 20 Nov 2020
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration Priyank Agrawal Jinglin Chen Nan Jiang 27 18 0 23 Oct 2020
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences Raphael Köster Kevin R. McKee Richard Everett Laura Weidinger William S. Isaac Edward Hughes Edgar A. Duénez-Guzmán T. Graepel M. Botvinick Joel Z Leibo 24 22 0 18 Oct 2020
Randomized Value Functions via Posterior State-Abstraction Sampling Dilip Arumugam Benjamin Van Roy OffRL 28 7 0 05 Oct 2020
Novelty Search in Representational Space for Sample Efficient Exploration Ruo Yu Tao Vincent François-Lavet Joelle Pineau 30 43 0 28 Sep 2020
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors Yuling Yao Aki Vehtari Andrew Gelman 29 60 0 22 Jun 2020
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration Zhenghao Peng Hao Sun Bolei Zhou 13 18 0 14 Jun 2020
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension Ruosong Wang Ruslan Salakhutdinov Lin F. Yang 23 55 0 21 May 2020
Adaptive Approximate Policy Iteration Botao Hao N. Lazić Yasin Abbasi-Yadkori Pooria Joulani Csaba Szepesvári 8 14 0 08 Feb 2020
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts Gilwoo Lee Brian Hou Sanjiban Choudhury S. Srinivasa BDL OffRL 28 7 0 07 Feb 2020
Making Sense of Reinforcement Learning and Probabilistic Inference Brendan O'Donoghue Ian Osband Catalin Ionescu OffRL 16 47 0 03 Jan 2020
Implicit Generative Modeling for Efficient Exploration Neale Ratzlaff Qinxun Bai Fuxin Li Wenyuan Xu 14 12 0 19 Nov 2019
Explicit Explore-Exploit Algorithms in Continuous State Spaces Mikael Henaff OffRL 14 31 0 01 Nov 2019
MAVEN: Multi-Agent Variational Exploration Anuj Mahajan Tabish Rashid Mikayel Samvelyan Shimon Whiteson DRL 137 355 0 16 Oct 2019
Behaviour Suite for Reinforcement Learning Ian Osband Yotam Doron Matteo Hessel John Aslanides Eren Sezener ... Satinder Singh Benjamin Van Roy R. Sutton David Silver H. V. Hasselt OffRL 24 178 0 09 Aug 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 35 185 0 05 Jun 2019
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow Hsuan-Kung Yang Po-Han Chiang Kuan-Wei Ho Min-Fong Hong Chun-Yi Lee 21 7 0 24 Jan 2019
A Short Survey on Probabilistic Reinforcement Learning R. Russel 11 2 0 21 Jan 2019
Optimization of Molecules via Deep Reinforcement Learning Zhenpeng Zhou S. Kearnes Li Li R. Zare Patrick F. Riley AI4CE 16 532 0 19 Oct 2018
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning Boris Ivanovic James Harrison Apoorva Sharma Mo Chen Marco Pavone OffRL 16 57 0 16 Jun 2018
Randomized Prior Functions for Deep Reinforcement Learning Ian Osband John Aslanides Albin Cassirer UQCV BDL 21 373 0 08 Jun 2018
Exploration by Distributional Reinforcement Learning Yunhao Tang Shipra Agrawal OOD 36 30 0 04 May 2018
Gaussian-Dirichlet Posterior Dominance in Sequential Learning Ian Osband Benjamin Van Roy 11 8 0 14 Feb 2017
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband Benjamin Van Roy BDL 6 255 0 01 Jul 2016