Evan Greensmith

Reinforcement Learning · 2001

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 13 citations into it; #576 of 2,242 by trust here, #952 by citations

Whom Evan Greensmith trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Ronald J. Williams
4. Peter W. Glynn
5. Leemon C. Baird III
6. Vijay R. Konda
7. Jonathan Baxter
8. Steven J. Bradtke
9. Leslie Pack Kaelbling
10. John N. Tsitsiklis

Who trusts Evan Greensmith overall

1. Huizhen Yu
2. Andriy Mnih
3. Sergey Levine
4. Aravind Rajeswaran
5. Pieter Abbeel
6. J. Kolter
7. Ching-An Cheng
8. Jan Peters
9. Jacob Andreas
10. Jie Tang

Papers from Evan Greensmith's perspective

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

1. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
2. Reinforcement Learning: An Introduction
3. Policy Gradient Methods for Reinforcement Learning with Function Approximation
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Learning to predict by the methods of temporal differences
8. Linear Least-Squares algorithms for temporal difference learning
9. Actor-Critic Algorithms
10. Planning and Acting in Partially Observable Stochastic Domains

Their papers

• Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning (2001) — 592 citations