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RL Unplugged: Benchmarks for Offline Reinforcement Learning

Jul 02, 2020
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

* 21 pages including supplementary material, the github link for the datasets: https://github.com/deepmind/deepmind-research/rl_unplugged 

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Conservative Q-Learning for Offline Reinforcement Learning

Jun 29, 2020
Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

* Preprint. Website at: https://sites.google.com/view/cql-offline-rl 

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DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Jun 18, 2020
Zhe Dong, Andriy Mnih, George Tucker


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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

May 04, 2020
Sergey Levine, Aviral Kumar, George Tucker, Justin Fu


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D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Apr 20, 2020
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine

* Website available at https://sites.google.com/view/d4rl/home 

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Datasets for Data-Driven Reinforcement Learning

Apr 15, 2020
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine


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Meta-Learning without Memorization

Dec 24, 2019
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn

* ICLR 2020 

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Behavior Regularized Offline Reinforcement Learning

Nov 26, 2019
Yifan Wu, George Tucker, Ofir Nachum


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Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse

Nov 06, 2019
James Lucas, George Tucker, Roger Grosse, Mohammad Norouzi

* 11 main pages, 10 appendix pages. 13 figures total. Accepted at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) 

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Energy-Inspired Models: Learning with Sampler-Induced Distributions

Oct 31, 2019
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath


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Reinforcement Learning Driven Heuristic Optimization

Jun 16, 2019
Qingpeng Cai, Will Hang, Azalia Mirhoseini, George Tucker, Jingtao Wang, Wei Wei

* DRL4KDD'19 

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Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Jun 03, 2019
Aviral Kumar, Justin Fu, George Tucker, Sergey Levine


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On Variational Bounds of Mutual Information

May 16, 2019
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A. Alemi, George Tucker

* ICML 2019 

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Learning to Walk via Deep Reinforcement Learning

Mar 25, 2019
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine

* Videos: https://sites.google.com/view/minitaur-locomotion/ . arXiv admin note: substantial text overlap with arXiv:1812.05905 

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Model-Based Reinforcement Learning for Atari

Mar 05, 2019
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Ryan Sepassi, George Tucker, Henryk Michalewski


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Soft Actor-Critic Algorithms and Applications

Jan 29, 2019
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine

* arXiv admin note: substantial text overlap with arXiv:1801.01290 

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The Laplacian in RL: Learning Representations with Efficient Approximations

Oct 10, 2018
Yifan Wu, George Tucker, Ofir Nachum


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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

Oct 09, 2018
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison


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Smoothed Action Value Functions for Learning Gaussian Policies

Jul 25, 2018
Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans

* ICML 2018 

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Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Jul 04, 2018
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee


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Guided evolutionary strategies: escaping the curse of dimensionality in random search

Jun 28, 2018
Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein


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The Mirage of Action-Dependent Baselines in Reinforcement Learning

Apr 06, 2018
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

* Updated to address comments from ICLR workshop reviewers 

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Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

Feb 26, 2018
Carlos Riquelme, George Tucker, Jasper Snoek

* Sixth International Conference on Learning Representations, ICLR 2018 

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Filtering Variational Objectives

Nov 12, 2017
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh


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REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

Nov 06, 2017
George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein

* NIPS 2017 

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