Satvik Sharma

Reinforcement Learning · 2021

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 30 citations into it
• Artificial Intelligence — 29 citations into it

Whom Satvik Sharma trusts overall

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

1. Daniel S. Brown
2. John Schulman
3. Martin L. Puterman
4. Pieter Abbeel
5. Jan Peters
6. Sergey Levine
7. Richard S. Sutton
8. Jonathan Ho

Papers from Satvik Sharma's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Trust Region Policy Optimization (International Conference on Machine Learning)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Proximal Policy Optimization Algorithms (arXiv.org)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. The Arcade Learning Environment: An Evaluation Platform for General Agents
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Policy Gradient Bayesian Robust Optimization for Imitation Learning (2021) — 20 citations