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Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation


Jun 24, 2021
Yunhao Tang, Tadashi Kozuno, Mark Rowland, R茅mi Munos, Michal Valko


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Taylor Expansion of Discount Factors


Jun 14, 2021
Yunhao Tang, Mark Rowland, R茅mi Munos, Michal Valko

* Accepted at International Conference of Machine Learning (ICML), 2021 

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Unlocking Pixels for Reinforcement Learning via Implicit Attention


Mar 04, 2021
Krzysztof Choromanski, Deepali Jain, Jack Parker-Holder, Xingyou Song, Valerii Likhosherstov, Anirban Santara, Aldo Pacchiano, Yunhao Tang, Adrian Weller


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Revisiting Peng's Q($位$) for Modern Reinforcement Learning


Feb 27, 2021
Tadashi Kozuno, Yunhao Tang, Mark Rowland, R茅mi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

* 26 pages, 7 figures, 2 tables 

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ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning


Jan 19, 2021
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Yuxiang Yang

* 14 pages. This is an updated version of a previous submission which can be found at arXiv:1907.06511. See https://github.com/google-research/google-research/tree/master/es_enas for associated code 

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Monte-Carlo Tree Search as Regularized Policy Optimization


Jul 24, 2020
Jean-Bastien Grill, Florent Altch茅, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, R茅mi Munos

* Accepted to International Conference on Machine Learning (ICML), 2020 

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Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies


Jun 13, 2020
Yunhao Tang, Krzysztof Choromanski


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Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning


Jun 13, 2020
Yunhao Tang, Alp Kucukelbir


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Self-Imitation Learning via Generalized Lower Bound Q-learning


Jun 12, 2020
Yunhao Tang


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Taylor Expansion Policy Optimization


Mar 13, 2020
Yunhao Tang, Michal Valko, R茅mi Munos


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Discrete Action On-Policy Learning with Action-Value Critic


Feb 21, 2020
Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou


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ES-MAML: Simple Hessian-Free Meta Learning


Oct 05, 2019
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang

* 10 main pages, 21 total pages, 21 figures 

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Reinforcement Learning with Chromatic Networks


Jul 10, 2019
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Deepali Jain, Yuxiang Yang

* 10 main pages, 22 total pages 

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Wasserstein Reinforcement Learning


Jun 19, 2019
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan


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Reinforcement Learning for Integer Programming: Learning to Cut


Jun 11, 2019
Yunhao Tang, Shipra Agrawal, Yuri Faenza


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Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes


May 29, 2019
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang


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Orthogonal Estimation of Wasserstein Distances


Apr 05, 2019
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller

* Published at AISTATS 2019 

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Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy


Mar 13, 2019
Yunhao Tang, Mingzhang Yin, Mingyuan Zhou


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Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces


Mar 12, 2019
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang


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Discretizing Continuous Action Space for On-Policy Optimization


Feb 01, 2019
Yunhao Tang, Shipra Agrawal


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Boosting Trust Region Policy Optimization by Normalizing Flows Policy


Sep 27, 2018
Yunhao Tang, Shipra Agrawal


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Exploration by Distributional Reinforcement Learning


Jun 21, 2018
Yunhao Tang, Shipra Agrawal

* IJCAI 2018 

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Implicit Policy for Reinforcement Learning


Jun 10, 2018
Yunhao Tang, Shipra Agrawal


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Variational Deep Q Network


Nov 30, 2017
Yunhao Tang, Alp Kucukelbir

* 12 pages, 5 figures, Second workshop on Bayesian Deep Learning (NIPS 2017) 

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