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Seohong Park — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Seohong Park
22
papers ·
28
citations ·
4
h-index
Google Scholar ↗
Semantic Scholar ↗
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Most-cited papers
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods
2021 · 7 citations
HIQL: Offline Goal-Conditioned RL with Latent States as Actions
2023 · 2 citations
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
2023 · 2 citations
Predictable MDP Abstraction for Unsupervised Model-Based RL
2023 · 1 citations
Is Value Learning Really the Main Bottleneck in Offline RL?
2024 · 1 citations
Scalable Offline Model-Based RL with Action Chunks
2025
Decoupled Q-Chunking
2025
Dual Goal Representations
2025
Transitive RL: Value Learning via Divide and Conquer
2025
Horizon Reduction Makes RL Scalable
2025
Intention-Conditioned Flow Occupancy Models
2025
Steering Your Diffusion Policy with Latent Space Reinforcement Learning
2025
Diffusion Guidance Is a Controllable Policy Improvement Operator
2025
Flow Q-Learning
2025
Foundation Policies with Hilbert Representations
2024
Top co-authors
Sergey Levine
· 17
Kevin Frans
· 5
Benjamin Eysenbach
· 4
Aviral Kumar
· 2
Deepinder Mann
· 2
Pieter Abbeel
· 2
Qiyang Li
· 2
Abhishek Gupta
· 1
Aditya Oberai
· 1
Andrew Wagenmaker
· 1
Anusha Nagabandi
· 1
Chongyi Zheng
· 1
Topics
Offline RL
Model-Based RL
Meta-RL
Exploration
cs.LG
cs.AI
Policy Gradient
Value-Based
cs.RO
stat.ML