Mobile device-based cellular network coverage analysis using crowd sourcing. - Daniel Jaymin Mankowitz; Andrew J. Paverd (EUROCON, 2011)

BRISK-Based Visual Feature Extraction for Resource Constrained Robots. - Daniel Jaymin Mankowitz; Subramanian Ramamoorthy (RoboCup, 2013)

Time-Regularized Interrupting Options (TRIO). - Timothy A. Mann; Daniel J. Mankowitz; Shie Mannor (ICML, 2014)

Learning When to Switch between Skills in a High Dimensional Domain. - Timothy A. Mann; Daniel J. Mankowitz; Shie Mannor (AAAI Workshop: Learning for General Competency in Video Games, 2015)

Bootstrapping Skills. - Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (CoRR, 2015)

CFORB: Circular FREAK-ORB Visual Odometry. - Daniel J. Mankowitz; Ehud Rivlin (CoRR, 2015)

Adaptive Skills Adaptive Partitions (ASAP). - Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (NIPS, 2016)

Iterative Hierarchical Optimization for Misspecified Problems (IHOMP). - Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (CoRR, 2016)

Situational Awareness by Risk-Conscious Skills. - Daniel J. Mankowitz; Aviv Tamar; Shie Mannor (CoRR, 2016)

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

Shallow Updates for Deep Reinforcement Learning. - Nir Levine; Tom Zahavy; Daniel J. Mankowitz; Aviv Tamar; Shie Mannor (NIPS, 2017)

Situationally Aware Options. - Daniel J. Mankowitz; Aviv Tamar; Shie Mannor (CoRR, 2017)

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

Learning How Not to Act in Text-based Games. - Matan Haroush; Tom Zahavy; Daniel J. Mankowitz; Shie Mannor (ICLR (Workshop), 2018)

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. - André Barreto 001; Diana Borsa; John Quan; Tom Schaul; David Silver; Matteo Hessel; Daniel J. Mankowitz; Augustin Zídek; Rémi Munos (ICML, 2018)

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

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

Unicorn: Continual Learning with a Universal, Off-policy Agent. - Daniel J. Mankowitz; Augustin Zídek; André Barreto 001; Dan Horgan; Matteo Hessel; John Quan; Junhyuk Oh; Hado van Hasselt; David Silver; Tom Schaul (CoRR, 2018)

Universal Successor Features Approximators. - Diana Borsa; André Barreto 001; John Quan; Daniel J. Mankowitz; Hado van Hasselt; Rémi Munos; David Silver; Tom Schaul (ICLR (Poster), 2019)

Reward Constrained Policy Optimization. - Chen Tessler; Daniel J. Mankowitz; Shie Mannor (ICLR (Poster), 2019)

A Bayesian Approach to Robust Reinforcement Learning. - Esther Derman; Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (UAI, 2019)

Challenges of Real-World Reinforcement Learning. - Gabriel Dulac-Arnold; Daniel J. Mankowitz; Todd Hester (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)

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)

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 (ICLR, 2020)

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Thomas Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (NeurIPS, 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: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (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; et al. (CoRR, 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)

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 (CoRL, 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 (ICLR, 2021)

Discovering a set of policies for the worst case reward. - Tom Zahavy; André Barreto 001; Daniel J. Mankowitz; Shaobo Hou; Brendan O'Donoghue; Iurii Kemaev; Satinder Singh 001 (ICLR, 2021)

Active Offline Policy Selection. - Ksenia Konyushkova; Yutian Chen 001; Thomas Paine; Çaglar Gülçehre; Cosmin Paduraru; Daniel J. Mankowitz; Misha Denil; Nando de Freitas (NeurIPS, 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)

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. - Jongmin Lee 004; Cosmin Paduraru; Daniel J. Mankowitz; Nicolas Heess; Doina Precup; Kee-Eung Kim; Arthur Guez (ICLR, 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; et al. (CoRR, 2022)

Competition-Level Code Generation with AlphaCode. - Yujia Li 001; David H. Choi; Junyoung Chung; Nate Kushman; Julian Schrittwieser; Rémi Leblond; Tom Eccles; James Keeling; Felix Gimeno; Agustin Dal Lago; Thomas Hubert; Peter Choy; et al. (CoRR, 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; et al. (CoRR, 2022)

Transformers Meet Directed Graphs. - Simon Geisler; Yujia Li 001; Daniel J. Mankowitz; Ali Taylan Cemgil; Stephan Günnemann; Cosmin Paduraru (ICML, 2023)

Optimizing Memory Mapping Using Deep Reinforcement Learning. - Pengming Wang 001; Mikita Sazanovich; Berkin Ilbeyi; Phitchaya Mangpo Phothilimthana; Manish Purohit; Han Yang Tay; Ngân Vu; Miaosen Wang; Cosmin Paduraru; Edouard Leurent; Anton Zhernov; Julian Schrittwieser; et al. (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; et al. (CoRR, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 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; et al. (Nat., 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)