| Lipschitz-constrained unsupervised skill discovery S Park, J Choi, J Kim, H Lee, G Kim International Conference on Learning Representations, 2022 | 48 | 2022 |
| Controllability-aware unsupervised skill discovery S Park, K Lee, Y Lee, P Abbeel arXiv preprint arXiv:2302.05103, 2023 | 35 | 2023 |
| Unsupervised skill discovery with bottleneck option learning J Kim, S Park, G Kim arXiv preprint arXiv:2106.14305, 2021 | 34 | 2021 |
| 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 | 22 | 2021 |
| 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 | 22 | 2018 |
| 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 | 21 | 2024 |
| Metra: Scalable unsupervised rl with metric-aware abstraction S Park, O Rybkin, S Levine arXiv preprint arXiv:2310.08887, 2023 | 16 | 2023 |
| Predictable mdp abstraction for unsupervised model-based rl S Park, S Levine International Conference on Machine Learning, 27246-27268, 2023 | 7 | 2023 |
| Foundation policies with hilbert representations S Park, T Kreiman, S Levine arXiv preprint arXiv:2402.15567, 2024 | 6 | 2024 |
| 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 | 5 | 2022 |
| Is Value Learning Really the Main Bottleneck in Offline RL? S Park, K Frans, S Levine, A Kumar arXiv preprint arXiv:2406.09329, 2024 | 2 | 2024 |
| Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings K Frans, S Park, P Abbeel, S Levine arXiv preprint arXiv:2402.17135, 2024 | 1 | 2024 |
| Unsupervised-to-Online Reinforcement Learning J Kim, S Park, S Levine arXiv preprint arXiv:2408.14785, 2024 | | 2024 |
