Seohong Park

According to our database, Seohong Park authored at least 20 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Steering Your Diffusion Policy with Latent Space Reinforcement Learning.

Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, Sergey Levine

CoRR, June, 2025

Intention-Conditioned Flow Occupancy Models.

Chongyi Zheng, Seohong Park, Sergey Levine, Benjamin Eysenbach

CoRR, June, 2025

Horizon Reduction Makes RL Scalable.

Seohong Park, Kevin Frans, Deepinder Mann, Benjamin Eysenbach, Aviral Kumar, Sergey Levine

CoRR, June, 2025

Diffusion Guidance Is a Controllable Policy Improvement Operator.

Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine

CoRR, May, 2025

Flow Q-Learning.

Seohong Park, Qiyang Li, Sergey Levine

CoRR, February, 2025

OGBench: Benchmarking Offline Goal-Conditioned RL.

Seohong Park, Kevin Frans, Benjamin Eysenbach, Sergey Levine

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

GHIL-Glue: Hierarchical Control with Filtered Subgoal Images.

Kyle Beltran Hatch, Ashwin Balakrishna, Oier Mees, Suraj Nair, Seohong Park, Blake Wulfe, Masha Itkina, Benjamin Eysenbach, Sergey Levine, Thomas Kollar, Benjamin Burchfiel

CoRR, 2024

Unsupervised-to-Online Reinforcement Learning.

Junsu Kim, Seohong Park, Sergey Levine

CoRR, 2024

Is Value Learning Really the Main Bottleneck in Offline RL?

Seohong Park, Kevin Frans, Sergey Levine, Aviral Kumar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Foundation Policies with Hilbert Representations.

Seohong Park, Tobias Kreiman, Sergey Levine

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings.

Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine

Proceedings of the Forty-first International Conference on Machine Learning, 2024

METRA: Scalable Unsupervised RL with Metric-Aware Abstraction.

Seohong Park, Oleh Rybkin, Sergey Levine

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

HIQL: Offline Goal-Conditioned RL with Latent States as Actions.

Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Controllability-Aware Unsupervised Skill Discovery.

Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel

Proceedings of the International Conference on Machine Learning, 2023

Predictable MDP Abstraction for Unsupervised Model-Based RL.

Seohong Park, Sergey Levine

Proceedings of the International Conference on Machine Learning, 2023

2022

Constrained GPI for Zero-Shot Transfer in Reinforcement Learning.

Jaekyeom Kim, Seohong Park, Gunhee Kim

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Lipschitz-constrained Unsupervised Skill Discovery.

Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods.

Seohong Park, Jaekyeom Kim, Gunhee Kim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unsupervised Skill Discovery with Bottleneck Option Learning.

Jaekyeom Kim, Seohong Park, Gunhee Kim

Proceedings of the 38th International Conference on Machine Learning, 2021

2018

On-the-fly workload partitioning for integrated CPU/GPU architectures.

Younghyun Cho, Florian Negele, Seohong Park, Bernhard Egger, Thomas R. Gross

Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques, 2018