Enrique Munoz de Cote

Reinforcement Learning · 2006–2017

A researcher in the Science corpus: 7 papers; trusts 60 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 109 citations into it
• Artificial Intelligence — 104 citations into it; #26,392 of 35,345 by trust here, #13,962 by citations
• Management Science and Operations Research — 49 citations into it; #7,605 of 11,946 by trust here, #2,667 by citations
• Game & Decision Theory — 27 citations into it
• Computer Networks and Communications — 19 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Control and Systems Engineering — 11 citations into it
• Electrical and Electronic Engineering — 11 citations into it

Whom Enrique Munoz de Cote trusts overall

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

1. Michael L. Littman
2. Martin L. Puterman
3. Richard S. Sutton
4. Junling Hu
5. Michael H. Bowling
6. Ryuichi Oka
7. Amy Greenwald
8. Andrew Barto
9. Ronald J. Williams
10. Yoshua Bengio

Who trusts Enrique Munoz de Cote overall

1. Pablo Hernandez-Leal
2. Jakob N. Foerster
3. Alex Peysakhovich
4. Adam Lerer
5. Sam Devlin
6. Edward Hughes
7. Joel Z. Leibo
8. Mark K. Ho
9. Richard Y. Chen
10. Sven Gronauer

Papers from Enrique Munoz de Cote's perspective

Ordered by how strongly the researchers Enrique Munoz de Cote trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Reinforcement Learning: An Introduction
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
9. ImageNet: A large-scale hierarchical image database
10. Asynchronous Methods for Deep Reinforcement Learning

Their papers

• A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity (2017) — 329 citations
• An exploration strategy for non-stationary opponents (2017) — 12 citations
• Learning to cooperate in multi-agent social dilemmas (2006) — 59 citations
• A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games (2008) — 36 citations
• Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning (2017) — 31 citations
• Diff-DAC: Distributed Actor-Critic for Multitask Deep Reinforcement Learning (2017) — 17 citations
• Social reward shaping in the prisoner's dilemma (2008) — 72 citations