Ani Harutyunyan

Reinforcement Learning · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 38 researchers and is trusted by 91 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 121 citations into it; #313 of 2,242 by trust here, #421 by citations
• Artificial Intelligence — 79 citations into it; #3,037 of 35,345 by trust here, #4,656 by citations
• Computational Theory and Mathematics — 10 citations into it; #1,113 of 12,182 by trust here, #557 by citations
• Electrical and Electronic Engineering — 7 citations into it
• Statistical Learning Theory — 7 citations into it
• Robotics & Control — 5 citations into it

Whom Ani Harutyunyan trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Satinder Singh
4. Doina Precup
5. Vivek Shripad Borkar
6. Ben J. A. Kröse
7. Dimitri P. Bertsekas
8. Martin L. Puterman
9. Jette Randløv
10. Gavin Adrian Rummery

Who trusts Ani Harutyunyan overall

1. R´emi Munos
2. Zhuangdi Zhu
3. Sergey Levine
4. Marc G. Bellemare
5. Aviral Kumar
6. A. Mahmood
7. Kaixiang Lin
8. Ahmed Touati
9. Cameron Voloshin
10. Yoshua Bengio

Papers from Ani Harutyunyan's perspective

Ordered by how strongly the researchers Ani Harutyunyan 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Reinforcement Learning: An Introduction
8. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Safe and Efficient Off-Policy Reinforcement Learning (2016) — 696 citations
• Q(λ) with Off-Policy Corrections (2016) — 105 citations
• Hindsight Credit Assignment (2019) — 95 citations
• Counterfactual Credit Assignment in Model-Free Reinforcement Learning (2020) — 73 citations
• Expressing Arbitrary Reward Functions as Potential-Based Advice (2015) — 134 citations
• Policy Transfer using Reward Shaping (2015) — 91 citations
• Reinforcement Learning from Demonstration through Shaping (2015) — 242 citations
• Q( λ ) with Off-Policy Corrections