Daniel Jaymin Mankowitz

Reinforcement Learning · 2014–2022

A researcher in the Science corpus: 16 papers; trusts 262 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 456 citations into it; #800 of 2,242 by trust here, #406 by citations
• Artificial Intelligence — 432 citations into it; #4,938 of 35,345 by trust here, #2,112 by citations
• Management Science and Operations Research — 90 citations into it; #2,949 of 11,946 by trust here, #1,077 by citations
• Computer Vision and Pattern Recognition — 81 citations into it
• Control and Systems Engineering — 75 citations into it
• Computational Theory and Mathematics — 73 citations into it; #8,815 of 12,182 by trust here, #5,465 by citations
• Natural Language Processing — 45 citations into it
• Evaluation & Benchmarks — 42 citations into it

Whom Daniel Jaymin Mankowitz trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Andrew Barto
3. Doina Precup
4. Geoffrey E. Hinton
5. Shie Mannor
6. Ronald J. Williams
7. Volodymyr Mnih
8. Satinder Singh
9. Yoshua Bengio
10. Sergey Levine

Who trusts Daniel Jaymin Mankowitz overall

1. Gabriel Dulac-Arnold
2. Cosmin Paduraru
3. Nir Levine
4. Timothy A. Mann
5. Shie Mannor
6. Tom Zahavy
7. Marlos C. Machado
8. Jerry Zheng Li
9. Michael Janner
10. Matthew Riemer

Papers from Daniel Jaymin Mankowitz's perspective

Ordered by how strongly the researchers Daniel Jaymin Mankowitz trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Deep Hierarchical Approach to Lifelong Learning in Minecraft (2016) — 404 citations
• Competition-level code generation with AlphaCode (2022) — 2,356 citations
• Challenges of Real-World Reinforcement Learning (2019) — 673 citations
• RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) — 74 citations
• Reward Constrained Policy Optimization (2018) — 717 citations
• Unicorn: Continual Learning with a Universal, Off-policy Agent (2018) — 48 citations
• Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning (2018) — 218 citations
• Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement (2018) — 192 citations
• Adaptive Skills Adaptive Partitions (ASAP) (2016) — 61 citations
• Learning Robust Options (2018) — 51 citations
• Challenges of real-world reinforcement learning: definitions, benchmarks and analysis (2021) — 780 citations
• Iterative Hierarchical Optimization for Misspecified Problems (IHOMP) (2016) — 10 citations
• MuZero with Self-competition for Rate Control in VP9 Video Compression (2022) — 48 citations
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020)
• An empirical investigation of the challenges of real-world reinforcement learning (2020) — 140 citations
• Time-regularized interrupting options (2014) — 23 citations