Junhyuk Oh

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 44

Top Topics

Policy Gradient

Reinforcement Learning

Multiarmed Bandit

Exploration Exploitation

Top Venues

Publications

Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan A. Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih
In-context Reinforcement Learning with Algorithm Distillation. ICLR (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. CoRR (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. NeurIPS (2023)
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
Introducing Symmetries to Black Box Meta Reinforcement Learning. AAAI (2022)
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan A. Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih
In-context Reinforcement Learning with Algorithm Distillation. CoRR (2022)
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity. CoRR (2021)
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
Introducing Symmetries to Black Box Meta Reinforcement Learning. CoRR (2021)
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Options via Meta-Learned Subgoals. CoRR (2021)
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity. AAMAS (2021)
Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann
Balancing Constraints and Rewards with Meta-Gradient D4PG. ICLR (2021)
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Options via Meta-Learned Subgoals. NeurIPS (2021)
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
Discovering Reinforcement Learning Algorithms. CoRR (2020)
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
A Self-Tuning Actor-Critic Algorithm. NeurIPS (2020)
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
What Can Learned Intrinsic Rewards Capture? ICML (2020)
Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann
Balancing Constraints and Rewards with Meta-Gradient D4PG. CoRR (2020)
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
Discovering Reinforcement Learning Algorithms. NeurIPS (2020)
Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
Meta-Gradient Reinforcement Learning with an Objective Discovered Online. NeurIPS (2020)
Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
Meta-Gradient Reinforcement Learning with an Objective Discovered Online. CoRR (2020)
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Self-Tuning Deep Reinforcement Learning. CoRR (2020)
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Useful Questions as Auxiliary Tasks. NeurIPS (2019)
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard L. Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Useful Questions as Auxiliary Tasks. CoRR (2019)
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
What Can Learned Intrinsic Rewards Capture? CoRR (2019)
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee
Contingency-Aware Exploration in Reinforcement Learning. ICLR (Poster) (2019)
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver
Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nat. 575 (7782) (2019)
Zeyu Zheng, Junhyuk Oh, Satinder Singh
On Learning Intrinsic Rewards for Policy Gradient Methods. CoRR (2018)
Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul
Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR (2018)
Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
Self-Imitation Learning. ICML (2018)
Vivek Veeriah, Junhyuk Oh, Satinder Singh
Many-Goals Reinforcement Learning. CoRR (2018)
Sungryull Sohn, Junhyuk Oh, Honglak Lee
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies. NeurIPS (2018)
Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
Self-Imitation Learning. CoRR (2018)
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee
Contingency-Aware Exploration in Reinforcement Learning. CoRR (2018)
Zeyu Zheng, Junhyuk Oh, Satinder Singh
On Learning Intrinsic Rewards for Policy Gradient Methods. NeurIPS (2018)
Yijie Guo, Junhyuk Oh, Satinder Singh, Honglak Lee
Generative Adversarial Self-Imitation Learning. CoRR (2018)
Sungryull Sohn, Junhyuk Oh, Honglak Lee
Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies. CoRR (2018)
Junhyuk Oh, Satinder P. Singh, Honglak Lee, Pushmeet Kohli
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. ICML (2017)
Junhyuk Oh, Satinder Singh, Honglak Lee
Value Prediction Network. CoRR (2017)
Junhyuk Oh, Satinder P. Singh, Honglak Lee, Pushmeet Kohli
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. CoRR (2017)
Junhyuk Oh, Satinder Singh, Honglak Lee
Value Prediction Network. NIPS (2017)
Junhyuk Oh, Valliappa Chockalingam, Satinder P. Singh, Honglak Lee
Control of Memory, Active Perception, and Action in Minecraft. CoRR (2016)
Seunghoon Hong, Junhyuk Oh, Honglak Lee, Bohyung Han
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. CVPR (2016)
Junhyuk Oh, Valliappa Chockalingam, Satinder P. Singh, Honglak Lee
Control of Memory, Active Perception, and Action in Minecraft. ICML (2016)
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder P. Singh
Action-Conditional Video Prediction using Deep Networks in Atari Games. NIPS (2015)
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder P. Singh
Action-Conditional Video Prediction using Deep Networks in Atari Games. CoRR (2015)
Seunghoon Hong, Junhyuk Oh, Bohyung Han, Honglak Lee
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. CoRR (2015)