关注
Seohong Park
Seohong Park
在 berkeley.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Lipschitz-constrained unsupervised skill discovery
S Park, J Choi, J Kim, H Lee, G Kim
International Conference on Learning Representations, 2022
462022
Controllability-aware unsupervised skill discovery
S Park, K Lee, Y Lee, P Abbeel
arXiv preprint arXiv:2302.05103, 2023
352023
Unsupervised skill discovery with bottleneck option learning
J Kim, S Park, G Kim
arXiv preprint arXiv:2106.14305, 2021
332021
On-the-fly workload partitioning for integrated CPU/GPU architectures
Y Cho, F Negele, S Park, B Egger, TR Gross
Proceedings of the 27th International Conference on Parallel Architectures …, 2018
222018
Time discretization-invariant safe action repetition for policy gradient methods
S Park, J Kim, G Kim
Advances in Neural Information Processing Systems 34, 267-279, 2021
212021
Hiql: Offline goal-conditioned rl with latent states as actions
S Park, D Ghosh, B Eysenbach, S Levine
Advances in Neural Information Processing Systems 36, 2024
202024
Metra: Scalable unsupervised rl with metric-aware abstraction
S Park, O Rybkin, S Levine
arXiv preprint arXiv:2310.08887, 2023
162023
Predictable mdp abstraction for unsupervised model-based rl
S Park, S Levine
International Conference on Machine Learning, 27246-27268, 2023
72023
Foundation policies with hilbert representations
S Park, T Kreiman, S Levine
arXiv preprint arXiv:2402.15567, 2024
62024
Constrained gpi for zero-shot transfer in reinforcement learning
J Kim, S Park, G Kim
Advances in Neural Information Processing Systems 35, 4585-4597, 2022
52022
Is Value Learning Really the Main Bottleneck in Offline RL?
S Park, K Frans, S Levine, A Kumar
arXiv preprint arXiv:2406.09329, 2024
12024
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
K Frans, S Park, P Abbeel, S Levine
arXiv preprint arXiv:2402.17135, 2024
12024
Unsupervised-to-Online Reinforcement Learning
J Kim, S Park, S Levine
arXiv preprint arXiv:2408.14785, 2024
2024
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