Wendelin Böhmer

Reinforcement Learning · 2012–2020

A researcher in the Science corpus: 13 papers; trusts 279 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 330 citations into it; #777 of 2,242 by trust here, #906 by citations
• Artificial Intelligence — 292 citations into it; #8,685 of 35,345 by trust here, #10,207 by citations
• Computational Theory and Mathematics — 65 citations into it; #12,178 of 12,182 by trust here, #9,306 by citations
• Management Science and Operations Research — 52 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it; #9,638 of 21,575 by trust here, #16,367 by citations
• Control and Systems Engineering — 46 citations into it
• Game & Decision Theory — 35 citations into it; #229 of 249 by trust here, #162 by citations
• Electrical and Electronic Engineering — 33 citations into it

Whom Wendelin Böhmer trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Andrew Barto
4. Yoshua Bengio
5. Jakob N. Foerster
6. Diederik P. Kingma
7. Andrew J. Davison
8. Alex Krizhevsky
9. Bernhard Schölkopf
10. Volodymyr Mnih

Who trusts Wendelin Böhmer overall

1. Christian Schröder de Witt
2. Tabish Rashid
3. Shimon Whiteson
4. Shariq Iqbal
5. Georgios Papoudakis
6. Bei Peng
7. Shangtong Zhang
8. Arrasy Rahman
9. Sergey Levine
10. Steffen Grünewälder

Papers from Wendelin Böhmer's perspective

Ordered by how strongly the researchers Wendelin Böhmer 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
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

• Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations (2015) — 70 citations
• Multitask Soft Option Learning (2019) — 26 citations
• Generalized Off-Policy Actor-Critic (2019) — 45 citations
• Deep Coordination Graphs (2019) — 219 citations
• Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning (2019) — 26 citations
• Multi-Agent Common Knowledge Reinforcement Learning (2018) — 119 citations
• FACMAC: Factored Multi-Agent Centralised Policy Gradients (2020) — 370 citations
• Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning (2020) — 88 citations
• AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning (2020) — 23 citations
• Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control (2020) — 100 citations
• Construction of approximation spaces for reinforcement learning (2013) — 23 citations
• Robot Navigation using Reinforcement Learning and Slow Feature Analysis (2012)
• Robot Navigation using Reinforcement Learning and Slow Feature Analysis (2012)