Rishabh Agarwal

Reinforcement Learning · with Bengio · 2020

A researcher in the Science corpus: 1 paper; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 38 citations into it
• Artificial Intelligence — 30 citations into it
• Management Science and Operations Research — 14 citations into it

Whom Rishabh Agarwal trusts overall

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

1. Richard S. Sutton
2. Marc G. Bellemare
3. Hado van Hasselt
4. Ben J. A. Kröse
5. Diederik P. Kingma
6. R´emi Munos
7. Andrew Barto
8. Doina Precup
9. Volodymyr Mnih
10. Geoffrey E. Hinton

Papers from Rishabh Agarwal's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Reinforcement Learning: An Introduction
3. Human-level control through deep reinforcement learning
4. The Arcade Learning Environment: An Evaluation Platform for General Agents
5. ImageNet classification with deep convolutional neural networks
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Playing Atari with Deep Reinforcement Learning (arXiv.org)
8. Gradient-based learning applied to document recognition
9. Attention is All you Need (Neural Information Processing Systems)
10. Prioritized Experience Replay

Their papers

• Revisiting Fundamentals of Experience Replay (2020) — 318 citations