Seohong Park

[Pronunciation: "suh-hong" ("Seo" as in "Seoul")]

Hello! I am a Ph.D. student at UC Berkeley advised by Sergey Levine. I am interested in developing better reinforcement learning (RL) algorithms and understanding their mathematical properties. More specifically, I am currently working on unsupervised RL, goal-conditioned RL, and offline RL.

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Publications and Preprints
(*: equal contribution)

	Foundation Policies with Hilbert Representations Seohong Park, Tobias Kreiman, Sergey Levine Preprint paper \| code \| thread \| project page
	Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine Preprint paper \| code \| thread \| project page
	METRA: Scalable Unsupervised RL with Metric-Aware Abstraction Seohong Park, Oleh Rybkin, Sergey Levine ICLR 2024 (Oral) paper \| code \| thread \| project page
	HIQL: Offline Goal-Conditioned RL with Latent States as Actions Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine NeurIPS 2023 (Spotlight) paper \| code \| thread \| talk (5 min) \| project page
	Controllability-Aware Unsupervised Skill Discovery Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel ICML 2023 paper \| code1 \| code2 \| thread \| project page
	Predictable MDP Abstraction for Unsupervised Model-Based RL Seohong Park, Sergey Levine ICML 2023 paper \| code \| thread \| project page
	Constrained GPI for Zero-Shot Transfer in Reinforcement Learning Jaekyeom Kim, Seohong Park, Gunhee Kim NeurIPS 2022 paper
	Lipschitz-constrained Unsupervised Skill Discovery Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim ICLR 2022 paper \| code \| thread \| project page
	Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods Seohong Park, Jaekyeom Kim, Gunhee Kim NeurIPS 2021 paper \| code
	Unsupervised Skill Discovery with Bottleneck Option Learning Jaekyeom Kim, Seohong Park, Gunhee Kim ICML 2021 paper \| code