Daniel J. Mankowitz

According to our database, Daniel J. Mankowitz authored at least 45 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Nash Learning from Human Feedback.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Faster sorting algorithms discovered using deep reinforcement learning.

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

Nat., 2023

Nash Learning from Human Feedback.

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

CoRR, 2023

Towards practical reinforcement learning for tokamak magnetic control.

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

CoRR, 2023

Optimizing Memory Mapping Using Deep Reinforcement Learning.

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

CoRR, 2023

Transformers Meet Directed Graphs.

Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru

Proceedings of the International Conference on Machine Learning, 2023

2022

Controlling Commercial Cooling Systems Using Reinforcement Learning.

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

CoRR, 2022

Competition-Level Code Generation with AlphaCode.

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

CoRR, 2022

MuZero with Self-competition for Rate Control in VP9 Video Compression.

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

CoRR, 2022

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation.

Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Challenges of real-world reinforcement learning: definitions, benchmarks and analysis.

Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

Mach. Learn., 2021

Active Offline Policy Selection.

Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Discovering a set of policies for the worst case reward.

Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh

Proceedings of the 9th International Conference on Learning Representations, 2021

Balancing Constraints and Rewards with Meta-Gradient D4PG.

Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann

Proceedings of the 9th International Conference on Learning Representations, 2021

A Constrained Multi-Objective Reinforcement Learning Framework.

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

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020

Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification.

Daniel J. Mankowitz, Dan A. Calian, Rae Jeong, Cosmin Paduraru, Nicolas Heess, Sumanth Dathathri, Martin A. Riedmiller, Timothy A. Mann

CoRR, 2020

Local Search for Policy Iteration in Continuous Control.

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

CoRR, 2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

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

CoRR, 2020

An empirical investigation of the challenges of real-world reinforcement learning.

Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

CoRR, 2020

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust Reinforcement Learning for Continuous Control with Model Misspecification.

Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy A. Mann, Martin A. Riedmiller

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Robust Reinforcement Learning for Continuous Control with Model Misspecification.

Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy A. Mann, Todd Hester, Martin A. Riedmiller

CoRR, 2019

Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces.

Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, Shie Mannor

CoRR, 2019

Challenges of Real-World Reinforcement Learning.

Gabriel Dulac-Arnold, Daniel J. Mankowitz, Todd Hester

CoRR, 2019

A Bayesian Approach to Robust Reinforcement Learning.

Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Reward Constrained Policy Optimization.

Chen Tessler, Daniel J. Mankowitz, Shie Mannor

Proceedings of the 7th International Conference on Learning Representations, 2019

Universal Successor Features Approximators.

Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Unicorn: Continual Learning with a Universal, Off-policy Agent.

Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul

CoRR, 2018

Soft-Robust Actor-Critic Policy-Gradient.

Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning.

Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning How Not to Act in Text-based Games.

Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Robust Options.

Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Situationally Aware Options.

Daniel J. Mankowitz, Aviv Tamar, Shie Mannor

CoRR, 2017

Shallow Updates for Deep Reinforcement Learning.

Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Deep Hierarchical Approach to Lifelong Learning in Minecraft.

Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Situational Awareness by Risk-Conscious Skills.

Daniel J. Mankowitz, Aviv Tamar, Shie Mannor

CoRR, 2016

Iterative Hierarchical Optimization for Misspecified Problems (IHOMP).

Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor

CoRR, 2016

Adaptive Skills Adaptive Partitions (ASAP).

Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

CFORB: Circular FREAK-ORB Visual Odometry.

Daniel J. Mankowitz, Ehud Rivlin

CoRR, 2015

Bootstrapping Skills.

Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor

CoRR, 2015

Learning When to Switch between Skills in a High Dimensional Domain.

Timothy A. Mann, Daniel J. Mankowitz, Shie Mannor

Proceedings of the Learning for General Competency in Video Games, 2015

2014

Time-Regularized Interrupting Options (TRIO).

Timothy A. Mann, Daniel J. Mankowitz, Shie Mannor

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

BRISK-Based Visual Feature Extraction for Resource Constrained Robots.

Daniel Jaymin Mankowitz, Subramanian Ramamoorthy

Proceedings of the RoboCup 2013: Robot World Cup XVII [papers from the 17th Annual RoboCup International Symposium, 2013

2011

Mobile device-based cellular network coverage analysis using crowd sourcing.

Daniel Jaymin Mankowitz, Andrew J. Paverd

Proceedings of EUROCON 2011, 2011