junhyuk (at) google (dot) com

Curriculum Vitae     Google Scholar

I am a research scientist at DeepMind. I finished my Ph.D. at the University of Michigan, where I was co-advised by Honglak Lee and Satinder Singh. My research focuses on deep reinforcement learning. Some of my work has been covered by MIT Technology Review and Daily Mail.

News

• May 2019: I will co-organize ICLR 2019 Workshop on Structure and Priors in Reinforcement Learning. 
• June 2018: I joined DeepMind as a research scientist.
• May 2018: I will co-organize ICML 2018 Workshop on Exploration in Reinforcement Learning.
• Sep 2017: I will co-organize NIPS 2017 Symposium on Deep Reinforcement Learning.
• June 2017: I will intern at DeepMind, advised by David Silver (Summer 2017).
• June 2016: I will intern at Microsoft Research, Redmond, advised by Pushmeet Kohli (Summer 2016).

Publications

Unicorn: Continual Learning with a Universal, Off-policy Agent

Daniel J. Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul

In Reinforcement Learning and Decision Making (RLDM), 2019. 

[Paper]

Contingency-Aware Exploration in Reinforcement Learning

Jongwook Choi*, Yijie Guo*, Marcin Moczulski*, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee

In International Conference on Learning Representations (ICLR), 2019. 

[Paper]

Generative Adversarial Self-Imitation Learning
Yijie Guo*, Junhyuk Oh*, Satinder Singh, Honglak Lee

In NeurIPS Workshop on Deep Reinforcement Learning, 2018.

[Paper]

Many-Goals Reinforcement Learning

Vivek Veeriah, Junhyuk Oh, Satinder Singh

In NeurIPS Workshop on Deep Reinforcement Learning, 2018.

[Paper]

On Learning Intrinsic Rewards for Policy Gradient Methods

Zeyu Zheng, Junhyuk Oh, Satinder Singh

In Neural Information Processing Systems (NeurIPS), 2018. 

[Paper] [Code]

Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies

Sungryull Sohn, Junhyuk Oh, Honglak Lee

In Neural Information Processing Systems (NeurIPS), 2018. 

[Paper]

Self-Imitation Learning

Junhyuk Oh*, Yijie Guo*, Satinder Singh, Honglak Lee

In International Conference on Machine Learning (ICML), 2018.

[Paper] [Code]

Value Prediction Network

Junhyuk Oh, Satinder Singh, Honglak Lee

In Neural Information Processing Systems (NeurIPS), 2017. 

[Paper] [Code]

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli

In International Conference on Machine Learning (ICML), 2017. 

[Paper] [Project Page]

Control of Memory, Active Perception, and Action in Minecraft

Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, Honglak Lee

In International Conference on Machine Learning (ICML), 2016. 

Press Coverage: [MIT Technology Review] [Daily Mail]

[Paper] [Project Page] [Code] 

Learning Transferrable Knowledge for Semantic Segmentation

 

with Deep Convolutional Neural Network 

Seunghoon Hong, Junhyuk Oh, Bohyung Han, Honglak Lee 

In Computer Vision and Pattern Recognition (CVPR), 2016.

Spotlight presentation

[Paper] [Project Page] [Code]

Action-Conditional Video Prediction using Deep Networks in Atari Games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, Satinder Singh
In Neural Information Processing Systems (NeurIPS), 2015. 

Spotlight presentation Acceptance rate: 4.5% (82/1858)

[Paper] [Project Page] [Code]