Gerhard Weiss

Reinforcement Learning · 1993–2017

A researcher in the Science corpus: 14 papers; trusts 51 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 58 citations into it; #1,242 of 2,242 by trust here, #1,253 by citations
• Artificial Intelligence — 53 citations into it; #32,864 of 35,345 by trust here, #14,718 by citations
• Management Science and Operations Research — 16 citations into it
• Game & Decision Theory — 14 citations into it
• Sociology and Political Science — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Information Systems — 9 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Gerhard Weiss trusts overall

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

1. Michael L. Littman
2. Michael H. Bowling
3. Richard S. Sutton
4. Matthew E. Taylor
5. Martin L. Puterman
6. Peter Stone
7. Sandip Sen
8. Andrew Barto
9. Martin A. Zinkevich
10. Maja J. Mataric

Who trusts Gerhard Weiss overall

1. Craig Boutilier
2. Gerhard Weiß
3. Caroline Claus
4. Karl Tuyls
5. Peter Stone
6. Sandip Sen
7. J. Urgen Schmidhuber
8. Maja J. Mataric
9. Sven Gronauer
10. Alessandro Farinelli

Papers from Gerhard Weiss's perspective

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

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

• Multiagent Learning: Basics, Challenges, and Prospects (2012) — 219 citations
• Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines (2013) — 26 citations
• Hierarchical reinforcement learning in communication-mediated multiagent coordination (2004) — 32 citations
• Multi-Agent Systems (2017) — 254 citations
• Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (2015) — 22 citations
• An automated measure of MDP similarity for transfer in reinforcement learning (2014) — 103 citations
• Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping (2011) — 37 citations
• Bee-inspired foraging in an embodied swarm (2011) — 17 citations
• Learning in multiagent systems (1999)
• Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments (1997)
• Adaptation and Learning in Multi-Agent Systems: Some Remarks and a Bibliography (1995)
• Adaption and Learning in Multi-Agent Systems (1995)
• Distributed reinforcement learning (1995)
• Learning to Coordinate Actions in Multi-Agent-Systems (1993)