S'ebastien Toth

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 16 citations into it
• Artificial Intelligence — 14 citations into it

Whom S'ebastien Toth trusts overall

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

1. Richard S. Sutton

Papers from S'ebastien Toth's perspective

Ordered by how strongly the researchers S'ebastien Toth trusts endorse each paper. Same corpus, a different order for every researcher.

1. Human-level control through deep reinforcement learning
2. Proximal Policy Optimization Algorithms (arXiv.org)
3. Policy Gradient Methods for Reinforcement Learning with Function Approximation
4. Domain randomization for transferring deep neural networks from simulation to the real world
5. ViZDoom: A Doom-based AI research platform for visual reinforcement learning
6. EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
7. Quantifying Generalization in Reinforcement Learning
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
10. ImageNet classification with deep convolutional neural networks

Their papers

• Robust Visual Domain Randomization for Reinforcement Learning. (2019) — 20 citations