Tuomas Haarnoja

Reinforcement Learning · 2016–2021

A researcher in the Science corpus: 8 papers; trusts 188 researchers and is trusted by 1,004 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 260 citations into it; #81 of 2,242 by trust here, #117 by citations
• Artificial Intelligence — 219 citations into it; #440 of 35,345 by trust here, #1,206 by citations
• Control and Systems Engineering — 74 citations into it; #7,024 of 8,257 by trust here, #3,389 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #2,550 of 21,575 by trust here, #3,848 by citations
• Biomedical Engineering — 38 citations into it
• Computational Theory and Mathematics — 35 citations into it
• Management Science and Operations Research — 31 citations into it
• Optimization & Training — 25 citations into it

Whom Tuomas Haarnoja trusts overall

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

1. E. Todorov
2. John Schulman
3. Richard S. Sutton
4. Sergey Levine
5. Ronald J. Williams
6. Pieter Abbeel
7. Geoffrey E. Hinton
8. Diederik P. Kingma
9. Volodymyr Mnih
10. Marc Toussaint

Who trusts Tuomas Haarnoja overall

1. Sergey Levine
2. Pieter Abbeel
3. Ofir Nachum
4. Chelsea Finn
5. Aviral Kumar
6. Ilya Kostrikov
7. Justin Fu
8. Benjamin Eysenbach
9. Abhishek Gupta
10. Yilun Du

Papers from Tuomas Haarnoja's perspective

Ordered by how strongly the researchers Tuomas Haarnoja 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) — 11,773 citations
• Reinforcement Learning with Deep Energy-Based Policies (2017) — 1,623 citations
• Soft Actor-Critic Algorithms and Applications (2018) — 3,200 citations
• Backprop KF: Learning Discriminative Deterministic State Estimators (2016) — 228 citations
• Latent Space Policies for Hierarchical Reinforcement Learning (2018) — 208 citations
• Composable Deep Reinforcement Learning for Robotic Manipulation (2018) — 254 citations
• Learning to Walk via Deep Reinforcement Learning (2018) — 501 citations
• From Motor Control to Team Play in Simulated Humanoid Football (2021) — 163 citations