Daniel J. Mankowitz

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 53

Top Topics

Reinforcement Learning

Sorting Algorithms

Top Venues

Publications

Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
Nash Learning from Human Feedback. CoRR (2023)
Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco Selvi, Cosmin Paduraru, Edouard Leurent, Shariq Iqbal, Jean-Baptiste Lespiau, Alex Ahern, Thomas Köppe, Kevin Millikin, Stephen Gaffney, Sophie Elster, Jackson Broshear, Chris Gamble, Kieran Milan, Robert Tung, Minjae Hwang, Taylan Cemgil, Mohammadamin Barekatain, Yujia Li, Amol Mandhane, Thomas Hubert, Julian Schrittwieser, Demis Hassabis, Pushmeet Kohli, Martin A. Riedmiller, Oriol Vinyals, David Silver
Faster sorting algorithms discovered using deep reinforcement learning. Nat. 618 (7964) (2023)
Brendan D. Tracey, Andrea Michi, Yuri Chervonyi, Ian Davies, Cosmin Paduraru, Nevena Lazic, Federico Felici, Timo Ewalds, Craig Donner, Cristian Galperti, Jonas Buchli, Michael Neunert, Andrea Huber, Jonathan Evens, Paula Kurylowicz, Daniel J. Mankowitz, Martin A. Riedmiller, The TCV Team
Towards practical reinforcement learning for tokamak magnetic control. CoRR (2023)
Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
Transformers Meet Directed Graphs. ICML (2023)
Pengming Wang, Mikita Sazanovich, Berkin Ilbeyi, Phitchaya Mangpo Phothilimthana, Manish Purohit, Han Yang Tay, Ngân Vu, Miaosen Wang, Cosmin Paduraru, Edouard Leurent, Anton Zhernov, Julian Schrittwieser, Thomas Hubert, Robert Tung, Paula Kurylowicz, Kieran Milan, Oriol Vinyals, Daniel J. Mankowitz
Optimizing Memory Mapping Using Deep Reinforcement Learning. CoRR (2023)
Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
Transformers Meet Directed Graphs. CoRR (2023)
Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. CoRR (2022)
Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. ICLR (2022)
Amol Mandhane, Anton Zhernov, Maribeth Rauh, Chenjie Gu, Miaosen Wang, Flora Xue, Wendy Shang, Derek Pang, Rene Claus, Ching-Han Chiang, Cheng Chen, Jingning Han, Angie Chen, Daniel J. Mankowitz, Jackson Broshear, Julian Schrittwieser, Thomas Hubert, Oriol Vinyals, Timothy A. Mann
MuZero with Self-competition for Rate Control in VP9 Video Compression. CoRR (2022)
Jerry Luo, Cosmin Paduraru, Octavian Voicu, Yuri Chervonyi, Scott Munns, Jerry Li, Crystal Qian, Praneet Dutta, Jared Quincy Davis, Ningjia Wu, Xingwei Yang, Chu-Ming Chang, Ted Li, Rob Rose, Mingyan Fan, Hootan Nakhost, Tinglin Liu, Brian Kirkman, Frank Altamura, Lee Cline, Patrick Tonker, Joel Gouker, Dave Uden, Warren Buddy Bryan, Jason Law, Deeni Fatiha, Neil Satra, Juliet Rothenberg, Molly Carlin, Satish Tallapaka, Sims Witherspoon, David Parish, Peter Dolan, Chenyu Zhao, Daniel J. Mankowitz
Controlling Commercial Cooling Systems Using Reinforcement Learning. CoRR (2022)
Yujia Li, David H. Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, Oriol Vinyals
Competition-Level Code Generation with AlphaCode. CoRR (2022)
Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas
Active Offline Policy Selection. NeurIPS (2021)
Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas
Active Offline Policy Selection. CoRR (2021)
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Mach. Learn. 110 (9) (2021)
Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Baveja Singh
Discovering a set of policies for the worst case reward. CoRR (2021)
Sandy H. Huang, Abbas Abdolmaleki, Giulia Vezzani, Philemon Brakel, Daniel J. Mankowitz, Michael Neunert, Steven Bohez, Yuval Tassa, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell
A Constrained Multi-Objective Reinforcement Learning Framework. CoRL (2021)
Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh
Discovering a set of policies for the worst case reward. ICLR (2021)
Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann
Balancing Constraints and Rewards with Meta-Gradient D4PG. ICLR (2021)
Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy A. Mann, Martin A. Riedmiller
Robust Reinforcement Learning for Continuous Control with Model Misspecification. ICLR (2020)
Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. NeurIPS (2020)
Daniel J. Mankowitz, Dan A. Calian, Rae Jeong, Cosmin Paduraru, Nicolas Heess, Sumanth Dathathri, Martin A. Riedmiller, Timothy A. Mann
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification. CoRR (2020)
Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas
RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR (2020)
Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann
Balancing Constraints and Rewards with Meta-Gradient D4PG. CoRR (2020)
Jost Tobias Springenberg, Nicolas Heess, Daniel J. Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin A. Riedmiller
Local Search for Policy Iteration in Continuous Control. CoRR (2020)
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester
An empirical investigation of the challenges of real-world reinforcement learning. CoRR (2020)
Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
A Bayesian Approach to Robust Reinforcement Learning. UAI (2019)
Gabriel Dulac-Arnold, Daniel J. Mankowitz, Todd Hester
Challenges of Real-World Reinforcement Learning. CoRR (2019)
Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
A Bayesian Approach to Robust Reinforcement Learning. CoRR (2019)
Chen Tessler, Daniel J. Mankowitz, Shie Mannor
Reward Constrained Policy Optimization. ICLR (Poster) (2019)
Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, Shie Mannor
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces. CoRR (2019)
Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy A. Mann, Todd Hester, Martin A. Riedmiller
Robust Reinforcement Learning for Continuous Control with Model Misspecification. CoRR (2019)
André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR (2019)
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul
Universal Successor Features Approximators. ICLR (Poster) (2019)
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. NeurIPS (2018)
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
Universal Successor Features Approximators. CoRR (2018)
Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul
Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR (2018)
Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
Learning How Not to Act in Text-based Games. ICLR (Workshop) (2018)
Chen Tessler, Daniel J. Mankowitz, Shie Mannor
Reward Constrained Policy Optimization. CoRR (2018)
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. CoRR (2018)
Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Soft-Robust Actor-Critic Policy-Gradient. UAI (2018)
Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor
Learning Robust Options. CoRR (2018)
Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Soft-Robust Actor-Critic Policy-Gradient. CoRR (2018)
Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor
Learning Robust Options. AAAI (2018)
André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML (2018)
Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Shallow Updates for Deep Reinforcement Learning. CoRR (2017)
Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Situationally Aware Options. CoRR (2017)
Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Shallow Updates for Deep Reinforcement Learning. NIPS (2017)
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. AAAI (2017)
Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Adaptive Skills, Adaptive Partitions (ASAP). CoRR (2016)
Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Adaptive Skills Adaptive Partitions (ASAP). NIPS (2016)
Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Situational Awareness by Risk-Conscious Skills. CoRR (2016)
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. CoRR (2016)
Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Iterative Hierarchical Optimization for Misspecified Problems (IHOMP). CoRR (2016)
Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Bootstrapping Skills. CoRR (2015)
Daniel J. Mankowitz, Ehud Rivlin
CFORB: Circular FREAK-ORB Visual Odometry. CoRR (2015)
Timothy A. Mann, Daniel J. Mankowitz, Shie Mannor
Learning When to Switch between Skills in a High Dimensional Domain. AAAI Workshop: Learning for General Competency in Video Games (2015)
Timothy A. Mann, Daniel J. Mankowitz, Shie Mannor
Time-Regularized Interrupting Options (TRIO). ICML (2014)