Mankowitz, Daniel J.

18 publications

ICML 2024 Nash Learning from Human Feedback Remi 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

ICML 2023 Transformers Meet Directed Graphs Simon Geisler, Yujia Li, Daniel J Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru

ICLR 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

CoRL 2021 A Constrained Multi-Objective Reinforcement Learning Framework Sandy Huang, Abbas Abdolmaleki, Giulia Vezzani, Philemon Brakel, Daniel J. Mankowitz, Michael Neunert, Steven Bohez, Yuval Tassa, Nicolas Heess, Martin Riedmiller, Raia Hadsell

NeurIPS 2021 Active Offline Policy Selection Ksenia Konyushova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas

ICLR 2021 Balancing Constraints and Rewards with Meta-Gradient D4PG Dan A. Calian, Daniel J Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy Mann

MLJ 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

ICLR 2021 Discovering a Set of Policies for the Worst Case Reward Tom Zahavy, Andre Barreto, Daniel J Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh

NeurIPS 2020 RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S Merel, Daniel J Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

ICLR 2020 Robust Reinforcement Learning for Continuous Control with Model Misspecification Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller

ICLR 2019 Reward Constrained Policy Optimization Chen Tessler, Daniel J. Mankowitz, Shie Mannor

ICLR 2019 Universal Successor Features Approximators Diana Borsa, Andre Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Remi Munos, David Silver, Tom Schaul

NeurIPS 2018 Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J Mankowitz, Shie Mannor

AAAI 2018 Learning Robust Options Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor

UAI 2018 Soft-Robust Actor-Critic Policy-Gradient Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor

AAAI 2017 A Deep Hierarchical Approach to Lifelong Learning in Minecraft Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor

NeurIPS 2017 Shallow Updates for Deep Reinforcement Learning Nir Levine, Tom Zahavy, Daniel J Mankowitz, Aviv Tamar, Shie Mannor

NeurIPS 2016 Adaptive Skills Adaptive Partitions (ASAP) Daniel J Mankowitz, Timothy A Mann, Shie Mannor