Action-Conditional Video Prediction using Deep Networks in Atari Games. - Junhyuk Oh; Xiaoxiao Guo; Honglak Lee; Richard L. Lewis; Satinder Singh 001 (NIPS, 2015)

Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. - Seunghoon Hong; Junhyuk Oh; Honglak Lee; Bohyung Han (CVPR, 2016)

Control of Memory, Active Perception, and Action in Minecraft. - Junhyuk Oh; Valliappa Chockalingam; Satinder Singh 001; Honglak Lee (ICML, 2016)

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. - Junhyuk Oh; Satinder Singh 001; Honglak Lee; Pushmeet Kohli (ICML, 2017)

Value Prediction Network. - Junhyuk Oh; Satinder Singh 001; Honglak Lee (NIPS, 2017)

Self-Imitation Learning. - Junhyuk Oh; Yijie Guo; Satinder Singh 001; Honglak Lee (ICML, 2018)

Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies. - Sungryull Sohn; Junhyuk Oh; Honglak Lee (NeurIPS, 2018)

On Learning Intrinsic Rewards for Policy Gradient Methods. - Zeyu Zheng; Junhyuk Oh; Satinder Singh 001 (NeurIPS, 2018)

Unicorn: Continual Learning with a Universal, Off-policy Agent. - Daniel J. Mankowitz; Augustin Zídek; André Barreto 001; Dan Horgan; Matteo Hessel; John Quan; Junhyuk Oh; Hado van Hasselt; David Silver; Tom Schaul (CoRR, 2018)

Many-Goals Reinforcement Learning. - Vivek Veeriah; Junhyuk Oh; Satinder Singh 001 (CoRR, 2018)

Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies. - Sungryull Sohn; Junhyuk Oh; Honglak Lee (CoRR, 2018)

Generative Adversarial Self-Imitation Learning. - Yijie Guo; Junhyuk Oh; Satinder Singh 001; Honglak Lee (CoRR, 2018)

Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration. - Junhyuk Oh (2018)

Contingency-Aware Exploration in Reinforcement Learning. - Jongwook Choi; Yijie Guo; Marcin Moczulski; Junhyuk Oh; Neal Wu; Mohammad Norouzi 002; Honglak Lee (ICLR (Poster), 2019)

Discovery of Useful Questions as Auxiliary Tasks. - Vivek Veeriah; Matteo Hessel; Zhongwen Xu; Janarthanan Rajendran; Richard L. Lewis; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (NeurIPS, 2019)

Grandmaster level in StarCraft II using multi-agent reinforcement learning. - 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; et al. (Nat., 2019)

What Can Learned Intrinsic Rewards Capture? - Zeyu Zheng; Junhyuk Oh; Matteo Hessel; Zhongwen Xu; Manuel Kroiss; Hado van Hasselt; David Silver; Satinder Singh 001 (ICML, 2020)

Discovering Reinforcement Learning Algorithms. - Junhyuk Oh; Matteo Hessel; Wojciech M. Czarnecki; Zhongwen Xu; Hado van Hasselt; Satinder Singh 001; David Silver (NeurIPS, 2020)

Meta-Gradient Reinforcement Learning with an Objective Discovered Online. - Zhongwen Xu; Hado Philip van Hasselt; Matteo Hessel; Junhyuk Oh; Satinder Singh 001; David Silver (NeurIPS, 2020)

A Self-Tuning Actor-Critic Algorithm. - Tom Zahavy; Zhongwen Xu; Vivek Veeriah; Matteo Hessel; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (NeurIPS, 2020)

Self-Tuning Deep Reinforcement Learning. - Tom Zahavy; Zhongwen Xu; Vivek Veeriah; Matteo Hessel; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (CoRR, 2020)

Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity. - Marta Garnelo; Wojciech Marian Czarnecki; Siqi Liu 002; Dhruva Tirumala; Junhyuk Oh; Gauthier Gidel; Hado van Hasselt; David Balduzzi (AAMAS, 2021)

Balancing Constraints and Rewards with Meta-Gradient D4PG. - Dan A. Calian; Daniel J. Mankowitz; Tom Zahavy; Zhongwen Xu; Junhyuk Oh; Nir Levine; Timothy A. Mann (ICLR, 2021)

Discovery of Options via Meta-Learned Subgoals. - Vivek Veeriah; Tom Zahavy; Matteo Hessel; Zhongwen Xu; Junhyuk Oh; Iurii Kemaev; Hado van Hasselt; David Silver; Satinder Singh 001 (NeurIPS, 2021)

Introducing Symmetries to Black Box Meta Reinforcement Learning. - Louis Kirsch; Sebastian Flennerhag; Hado van Hasselt; Abram L. Friesen; Junhyuk Oh; Yutian Chen 001 (AAAI, 2022)

In-context Reinforcement Learning with Algorithm Distillation. - Michael Laskin; Luyu Wang; Junhyuk Oh; Emilio Parisotto; Stephen Spencer; Richie Steigerwald; DJ Strouse; Steven Stenberg Hansen; Angelos Filos; Ethan Brooks; Maxime Gazeau; Himanshu Sahni; et al. (ICLR, 2023)

Deep Reinforcement Learning with Plasticity Injection. - Evgenii Nikishin; Junhyuk Oh; Georg Ostrovski; Clare Lyle; Razvan Pascanu; Will Dabney; André Barreto 001 (NeurIPS, 2023)

Preference Optimization as Probabilistic Inference. - Abbas Abdolmaleki; Bilal Piot; Bobak Shahriari; Jost Tobias Springenberg; Tim Hertweck; Rishabh Joshi; Junhyuk Oh; Michael Bloesch; Thomas Lampe; Nicolas Heess; Jonas Buchli; Martin A. Riedmiller (CoRR, 2024)

Learning from negative feedback, or positive feedback or both. - Abbas Abdolmaleki; Bilal Piot; Bobak Shahriari; Jost Tobias Springenberg; Tim Hertweck; Michael Bloesch; Rishabh Joshi; Thomas Lampe; Junhyuk Oh; Nicolas Heess; Jonas Buchli; Martin A. Riedmiller (ICLR, 2025)

DataRater: Meta-Learned Dataset Curation. - Dan A. Calian; Gregory Farquhar; Iurii Kemaev; Luisa M. Zintgraf; Matteo Hessel; Jeremy Shar; Junhyuk Oh; András György 001; Tom Schaul; Jeffrey Dean; Hado van Hasselt; David Silver (CoRR, 2025)