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Behavior Priors for Efficient Reinforcement Learning


Oct 27, 2020
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

* Submitted to Journal of Machine Learning Research (JMLR) 

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Importance Weighted Policy Learning and Adaption


Sep 10, 2020
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess


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A Distributional View on Multi-Objective Policy Optimization


May 15, 2020
Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller


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Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning


Apr 23, 2020
Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber


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Reusable neural skill embeddings for vision-guided whole body movement and object manipulation


Nov 15, 2019
Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess


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Meta reinforcement learning as task inference


May 15, 2019
Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess


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Information asymmetry in KL-regularized RL


May 03, 2019
Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

* Accepted as a conference paper at ICLR 2019 

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Exploiting Hierarchy for Learning and Transfer in KL-regularized RL


Mar 18, 2019
Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess


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Neural probabilistic motor primitives for humanoid control


Jan 15, 2019
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

* Accepted as a conference paper at ICLR 2019 

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Mix&Match - Agent Curricula for Reinforcement Learning


Jun 05, 2018
Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu

* ICML 2018 

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Sylvester Normalizing Flows for Variational Inference


Mar 15, 2018
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

* 12 pages, 4 figures 

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Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server


Sep 07, 2017
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

* Journal of Machine Learning Research 18 (2017) 1-37 
* 37 pages, 7 figures 

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Relativistic Monte Carlo


Sep 14, 2016
Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer


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