Rein Houthooft

Researcher, Netflix

Joined

December 2016

Names

Rein Houthooft (Preferred)

Emails

****@ugent.be (Confirmed)

****@gmail.com (Confirmed)

****@openai.com (Confirmed)

Personal Links

DBLP

Career & Education History

Researcher

Netflix (netflix.com)

2020 – 2026

Advisors, Relations & Conflicts

No relations added

Expertise

No areas of expertise listed

Publications

Evolved Policy Gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel
- 2018 (modified: 18 Nov 2022)
- NeurIPS 2018
- Readers: Everyone
The Importance of Sampling inMeta-Reinforcement Learning
Bradly C. Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
- 2018 (modified: 11 Nov 2022)
- NeurIPS 2018
- Readers: Everyone
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
- 2017 (modified: 11 Nov 2022)
- NIPS 2017
- Readers: Everyone
Structured Output Prediction for Semantic Perception in Autonomous Vehicles
Rein Houthooft, Cedric De Boom, Stijn Verstichel, Femke Ongenae, Filip De Turck
- 2016 (modified: 16 Jul 2019)
- AAAI 2016
- Readers: Everyone
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
- 2016 (modified: 11 Nov 2022)
- NIPS 2016
- Readers: Everyone
VIME: Variational Information Maximizing Exploration
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
- 2016 (modified: 11 Nov 2022)
- NIPS 2016
- Readers: Everyone
Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
- 2016 (modified: 11 Nov 2022)
- ICML 2016
- Readers: Everyone

Rein Houthooft

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

Evolved Policy Gradients

The Importance of Sampling inMeta-Reinforcement Learning

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Structured Output Prediction for Semantic Perception in Autonomous Vehicles

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

VIME: Variational Information Maximizing Exploration

Benchmarking Deep Reinforcement Learning for Continuous Control

Co-Authors