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ORCIDDaniel J. Mankowitz
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2020 – today
- 2024
[c24]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. ICML 2024- 2023
- [j2]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, A. 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): 257-263 (2023) - [c23]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. ICML 2023: 11144-11172 - [i34]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. CoRR abs/2302.00049 (2023) - [i33]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 abs/2305.07440 (2023) - [i32]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 abs/2307.11546 (2023) - [i31]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 abs/2312.00886 (2023) - 2022
- [c22]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 - [i30]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 abs/2202.06626 (2022) - [i29]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 abs/2203.07814 (2022) - [i28]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 abs/2204.08957 (2022) - [i27]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 abs/2211.07357 (2022) - 2021
- [j1]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): 2419-2468 (2021) - [c21]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: 883-893 - [c20]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 - [c19]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 - [c18]Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas:
Active Offline Policy Selection. NeurIPS 2021: 24631-24644 - [i26]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. CoRR abs/2102.04323 (2021) - [i25]Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas:
Active Offline Policy Selection. CoRR abs/2106.10251 (2021) - 2020
- [c17]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 - [c16]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 - [i24]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 abs/2003.11881 (2020) - [i23]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 abs/2006.13888 (2020) - [i22]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 abs/2010.05545 (2020) - [i21]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 abs/2010.06324 (2020) - [i20]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 abs/2010.10644 (2020)
2010 – 2019
- 2019
- [c15]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 - [c14]Chen Tessler, Daniel J. Mankowitz, Shie Mannor:
Reward Constrained Policy Optimization. ICLR (Poster) 2019 - [c13]Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
A Bayesian Approach to Robust Reinforcement Learning. UAI 2019: 648-658 - [i19]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 abs/1901.10964 (2019) - [i18]Gabriel Dulac-Arnold, Daniel J. Mankowitz, Todd Hester:
Challenges of Real-World Reinforcement Learning. CoRR abs/1904.12901 (2019) - [i17]Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
A Bayesian Approach to Robust Reinforcement Learning. CoRR abs/1905.08188 (2019) - [i16]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 abs/1905.09700 (2019) - [i15]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 abs/1906.07516 (2019) - 2018
- [c12]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. AAAI 2018: 6409-6416 - [c11]Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
Learning How Not to Act in Text-based Games. ICLR (Workshop) 2018 - [c10]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: 510-519 - [c9]Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor:
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. NeurIPS 2018: 3566-3577 - [c8]Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Soft-Robust Actor-Critic Policy-Gradient. UAI 2018: 208-218 - [i14]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. CoRR abs/1802.03236 (2018) - [i13]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 abs/1802.08294 (2018) - [i12]Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Soft-Robust Actor-Critic Policy-Gradient. CoRR abs/1803.04848 (2018) - [i11]Chen Tessler, Daniel J. Mankowitz, Shie Mannor:
Reward Constrained Policy Optimization. CoRR abs/1805.11074 (2018) - [i10]Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor:
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. CoRR abs/1809.02121 (2018) - [i9]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul:
Universal Successor Features Approximators. CoRR abs/1812.07626 (2018) - 2017
- [c7]Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. AAAI 2017: 1553-1561 - [c6]Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor:
Shallow Updates for Deep Reinforcement Learning. NIPS 2017: 3135-3145 - [i8]Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor:
Shallow Updates for Deep Reinforcement Learning. CoRR abs/1705.07461 (2017) - [i7]Daniel J. Mankowitz, Aviv Tamar, Shie Mannor:
Situationally Aware Options. CoRR abs/1711.07832 (2017) - 2016
- [c5]Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Adaptive Skills Adaptive Partitions (ASAP). NIPS 2016: 1588-1596 - [i6]Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Iterative Hierarchical Optimization for Misspecified Problems (IHOMP). CoRR abs/1602.03348 (2016) - [i5]Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Adaptive Skills, Adaptive Partitions (ASAP). CoRR abs/1602.03351 (2016) - [i4]Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. CoRR abs/1604.07255 (2016) - [i3]Daniel J. Mankowitz, Aviv Tamar, Shie Mannor:
Situational Awareness by Risk-Conscious Skills. CoRR abs/1610.02847 (2016) - 2015
- [c4]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 - [i2]Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Bootstrapping Skills. CoRR abs/1506.03624 (2015) - [i1]Daniel J. Mankowitz, Ehud Rivlin:
CFORB: Circular FREAK-ORB Visual Odometry. CoRR abs/1506.05257 (2015) - 2014
- [c3]Timothy A. Mann, Daniel J. Mankowitz, Shie Mannor:
Time-Regularized Interrupting Options (TRIO). ICML 2014: 1350-1358 - 2013
- [c2]Daniel Jaymin Mankowitz, Subramanian Ramamoorthy:
BRISK-Based Visual Feature Extraction for Resource Constrained Robots. RoboCup 2013: 195-206 - 2011
- [c1]Daniel Jaymin Mankowitz, Andrew J. Paverd:
Mobile device-based cellular network coverage analysis using crowd sourcing. EUROCON 2011: 1-6
Coauthor Index
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last updated on 2024-10-07 22:14 CEST by the dblp team
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