| Message-dropout: An efficient training method for multi-agent deep reinforcement learning W Kim, M Cho, Y Sung Proceedings of the AAAI conference on artificial intelligence 33 (01), 6079-6086, 2019 | 57 | 2019 |
| A maximum mutual information framework for multi-agent reinforcement learning W Kim, W Jung, M Cho, Y Sung arXiv preprint arXiv:2006.02732, 2020 | 15 | 2020 |
| Robust imitation learning against variations in environment dynamics J Chae, S Han, W Jung, M Cho, S Choi, Y Sung International Conference on Machine Learning, 2828-2852, 2022 | 9 | 2022 |
| Ph. D. student Y Sung, J Seo, J So, J Chung, S Bae, S Han, W Jung, W Kim, H Noh, ... PHD, 2015 | 7 | 2015 |
| A variational approach to mutual information-based coordination for multi-agent reinforcement learning W Kim, W Jung, M Cho, Y Sung arXiv preprint arXiv:2303.00451, 2023 | 5 | 2023 |
| Multi-task reinforcement learning with task representation method M Cho, W Jung, Y Sung ICLR 2022 Workshop on Generalizable Policy Learning in Physical World, 2022 | 5 | 2022 |
| Quantile constrained reinforcement learning: A reinforcement learning framework constraining outage probability W Jung, M Cho, J Park, Y Sung Advances in Neural Information Processing Systems 35, 6437-6449, 2022 | 3 | 2022 |
| 5G NSA 를 위한 링크 레벨 시뮬레이터 박기승， 박상우， 서준영， 소정호， 왕우완， 유승민， 임승찬， 정지훈， ... 제 28 회 통신정보합동학술대회, 2018 | 1 | 2018 |
| 5G NR 을 위한 모듈화 링크 레벨 시뮬레이터 설계 박기승， 박상우， 서준영， 소정호， 왕우완， 유승민， 임승찬， 정지훈， ... 한국통신학회 학술대회논문집, 352-353, 2018 | 1 | 2018 |
| Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition S Lee, M Cho, Y Sung Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| 5G K-SimLink: 5G NSA 를 위한 개방형 모듈화 링크레벨 시뮬레이터 개발 정지훈， 박상우， 왕우완， 박기승， 유승민， 임승찬， 조명식， 하대한， ... The Journal of Korean Institute of Communications and Information Sciences 3 …, 2019 | | 2019 |
| 5G 링크 레벨 시뮬레이터의 구현 박기승， 박상우， 서준영， 소정호， 왕우완， 유승민， 임승찬， 정지훈， ... 한국통신학회 학술대회논문집, 325-326, 2018 | | 2018 |
