Seohong Park - RankMe

37 papers A* 14B 1Journal 22

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Decoupled Q-Chunking. CoRR Qiyang Li, Seohong Park, Sergey Levine
2025	J	`jnl`	Diffusion Guidance Is a Controllable Policy Improvement Operator. CoRR Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine
2025	J	`jnl`	Dual Goal Representations. CoRR Seohong Park, Deepinder Mann, Sergey Levine
2025	A*	`conf`	Flow Q-Learning. ICML Seohong Park, Qiyang Li, Sergey Levine
2025	J	`jnl`	Flow Q-Learning. CoRR Seohong Park, Qiyang Li, Sergey Levine
2025	A*	`conf`	GHIL-Glue: Hierarchical Control with Filtered Subgoal Images. ICRA Kyle Beltran Hatch, Ashwin Balakrishna, Oier Mees, Suraj Nair, Seohong Park, Blake Wulfe, Masha Itkina, Benjamin Eysenbach, Sergey Levine, Thomas Kollar, Benjamin Burchfiel
2025	J	`jnl`	Horizon Reduction Makes RL Scalable. CoRR Seohong Park, Kevin Frans, Deepinder Mann, Benjamin Eysenbach, Aviral Kumar, Sergey Levine
2025	J	`jnl`	Intention-Conditioned Flow Occupancy Models. CoRR Chongyi Zheng, Seohong Park, Sergey Levine, Benjamin Eysenbach
2025	A*	`conf`	OGBench: Benchmarking Offline Goal-Conditioned RL. ICLR Seohong Park, Kevin Frans, Benjamin Eysenbach, Sergey Levine
2025	J	`jnl`	Scalable Offline Model-Based RL with Action Chunks. CoRR Kwanyoung Park, Seohong Park, Youngwoon Lee, Sergey Levine
2025	J	`jnl`	Steering Your Diffusion Policy with Latent Space Reinforcement Learning. CoRR Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, Sergey Levine
2025	J	`jnl`	Transitive RL: Value Learning via Divide and Conquer. CoRR Seohong Park, Aditya Oberai, Pranav Atreya, Sergey Levine
2024	A*	`conf`	Foundation Policies with Hilbert Representations. ICML Seohong Park, Tobias Kreiman, Sergey Levine
2024	J	`jnl`	Foundation Policies with Hilbert Representations. CoRR Seohong Park, Tobias Kreiman, Sergey Levine
2024	J	`jnl`	GHIL-Glue: Hierarchical Control with Filtered Subgoal Images. CoRR Kyle Beltran Hatch, Ashwin Balakrishna, Oier Mees, Suraj Nair, Seohong Park, Blake Wulfe, Masha Itkina, Benjamin Eysenbach, Sergey Levine, Thomas Kollar, Benjamin Burchfiel
2024	A*	`conf`	Is Value Learning Really the Main Bottleneck in Offline RL? NeurIPS Seohong Park, Kevin Frans, Sergey Levine, Aviral Kumar
2024	J	`jnl`	Is Value Learning Really the Main Bottleneck in Offline RL? CoRR Seohong Park, Kevin Frans, Sergey Levine, Aviral Kumar
2024	A*	`conf`	METRA: Scalable Unsupervised RL with Metric-Aware Abstraction. ICLR Seohong Park, Oleh Rybkin, Sergey Levine
2024	J	`jnl`	OGBench: Benchmarking Offline Goal-Conditioned RL. CoRR Seohong Park, Kevin Frans, Benjamin Eysenbach, Sergey Levine
2024	A*	`conf`	Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. ICML Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine
2024	J	`jnl`	Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. CoRR Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine
2024	J	`jnl`	Unsupervised-to-Online Reinforcement Learning. CoRR Junsu Kim, Seohong Park, Sergey Levine
2023	A*	`conf`	Controllability-Aware Unsupervised Skill Discovery. ICML Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel
2023	J	`jnl`	Controllability-Aware Unsupervised Skill Discovery. CoRR Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel
2023	A*	`conf`	HIQL: Offline Goal-Conditioned RL with Latent States as Actions. NeurIPS Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine
2023	J	`jnl`	HIQL: Offline Goal-Conditioned RL with Latent States as Actions. CoRR Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine
2023	J	`jnl`	METRA: Scalable Unsupervised RL with Metric-Aware Abstraction. CoRR Seohong Park, Oleh Rybkin, Sergey Levine
2023	A*	`conf`	Predictable MDP Abstraction for Unsupervised Model-Based RL. ICML Seohong Park, Sergey Levine
2023	J	`jnl`	Predictable MDP Abstraction for Unsupervised Model-Based RL. CoRR Seohong Park, Sergey Levine
2022	A*	`conf`	Constrained GPI for Zero-Shot Transfer in Reinforcement Learning. NeurIPS Jaekyeom Kim, Seohong Park, Gunhee Kim
2022	A*	`conf`	Lipschitz-constrained Unsupervised Skill Discovery. ICLR Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim
2022	J	`jnl`	Lipschitz-constrained Unsupervised Skill Discovery. CoRR Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim
2021	A*	`conf`	Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods. NeurIPS Seohong Park, Jaekyeom Kim, Gunhee Kim
2021	J	`jnl`	Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods. CoRR Seohong Park, Jaekyeom Kim, Gunhee Kim
2021	A*	`conf`	Unsupervised Skill Discovery with Bottleneck Option Learning. ICML Jaekyeom Kim, Seohong Park, Gunhee Kim
2021	J	`jnl`	Unsupervised Skill Discovery with Bottleneck Option Learning. CoRR Jaekyeom Kim, Seohong Park, Gunhee Kim
2018	B	`conf`	On-the-fly workload partitioning for integrated CPU/GPU architectures. PACT Younghyun Cho, Florian Negele, Seohong Park, Bernhard Egger, Thomas R. Gross