Sean Luke
Reinforcement Learning · 2004–2018
A researcher in the Science corpus: 11 papers; trusts 110 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Reinforcement Learning — 109 citations into it; #409 of 2,242 by trust here, #585 by citations
- Artificial Intelligence — 73 citations into it; #5,437 of 35,345 by trust here, #6,330 by citations
- Management Science and Operations Research — 40 citations into it; #2,908 of 11,946 by trust here, #2,603 by citations
- Game & Decision Theory — 28 citations into it; #68 of 249 by trust here, #112 by citations
- Control and Systems Engineering — 10 citations into it
- Computational Theory and Mathematics — 9 citations into it
- Computer Networks and Communications — 9 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
Whom Sean Luke trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michael L. Littman
- Liviu Panait
- Michael H. Bowling
- Richard S. Sutton
- Caroline Claus
- Mitchell A. Potter
- Craig Boutilier
- Spiros Kapetanakis
- Maja J. Mataric
- Ronald J. Williams
Who trusts Sean Luke overall
- Ermo Wei
- Karl Tuyls
- Gregory Palmer
- Sven Gronauer
- Liviu Panait
- Akifumi Wachi
- Amy H. Auchincloss
- Eduardo Rodrigues Gomes
- Laëtitia Matignon
- Shimon Whiteson
Papers from Sean Luke's perspective
Ordered by how strongly the researchers Sean Luke trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Reinforcement Learning: An Introduction
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Cooperative Multi-Agent Learning: The State of the Art (2005) — 1,452 citations
- Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space (2018) — 37 citations
- Multiagent Soft Q-Learning (2018) — 87 citations
- Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective (2008) — 98 citations
- Biasing Coevolutionary Search for Optimal Multiagent Behaviors (2006) — 101 citations
- An Overview of Cooperative and Competitive Multiagent Learning (2005) — 91 citations
- Lenient Learning in Independent-Learner Stochastic Cooperative Games (2016) — 107 citations
- Lenient learners in cooperative multiagent systems (2006) — 78 citations
- MASON: A Multiagent Simulation Environment (2005) — 1,023 citations
- Lenience towards Teammates Helps in Cooperative Multiagent Learning (2005) — 20 citations
- MASON: A New Multi-Agent Simulation Toolkit (2004) — 358 citations