Rodrigo Toro Icarte

Reinforcement Learning · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 52 citations into it
• Artificial Intelligence — 42 citations into it
• Control and Systems Engineering — 11 citations into it
• Information Systems — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Rodrigo Toro Icarte trusts overall

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

1. Richard S. Sutton
2. Satinder Singh
3. Thomas G. Dietterich
4. Diederik P. Kingma
5. Andrew Y. Ng
6. Ronald E. Parr
7. Geoffrey E. Hinton
8. Hado van Hasselt
9. Stuart J. Russell
10. Doina Precup

Who trusts Rodrigo Toro Icarte overall

1. Yuqian Jiang
2. Toryn Q. Klassen

Papers from Rodrigo Toro Icarte's perspective

Ordered by how strongly the researchers Rodrigo Toro Icarte 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• LTL2Action: Generalizing LTL Instructions for Multi-Task RL (2021) — 113 citations
• Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning (2020) — 325 citations
• Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning (2018) — 359 citations