Rajesh Kumar

Reinforcement Learning · with Singh · 2021

A researcher in the Science corpus: 1 paper; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 42 citations into it
• Artificial Intelligence — 30 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Robotics & Control — 5 citations into it

Whom Rajesh Kumar trusts overall

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

1. John N. Tsitsiklis
2. Leemon C. Baird III
3. Jan Peters
4. Geoffrey J. Gordon
5. Justin A. Boyan
6. Richard S. Sutton
7. Ian H. Witten
8. Marc Peter Deisenroth
9. Vijay R. Konda
10. Dimitri P. Bertsekas

Papers from Rajesh Kumar's perspective

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

1. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
2. ImageNet classification with deep convolutional neural networks
3. Reinforcement Learning: An Introduction
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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Markov Decision Processes: Discrete Stochastic Dynamic Programming
9. Residual Algorithms: Reinforcement Learning with Function Approximation
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Reinforcement learning in robotic applications: a comprehensive survey (2021) — 426 citations