5G K-Simulator: Link Level Simulator for 5G. - Giseung Park; Sangwoo Park 002; Junyeong Seo; Jungho So; Woowan Wang; Seungmin Yoo; Seung-Chan Lim; Jihoon Chung; Myungsik Cho; Daehan Ha; Joonhyuk Kang; Hyuncheol Park; et al. (DySPAN, 2018)

Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning. - Woojun Kim; Myungsik Cho; Youngchul Sung (AAAI, 2019)

A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning. - Woojun Kim; Whiyoung Jung; Myungsik Cho; Youngchul Sung (CoRR, 2020)

Robust Imitation Learning against Variations in Environment Dynamics. - Jongseong Chae; Seungyul Han; Whiyoung Jung; Myungsik Cho; Sungho Choi; Youngchul Sung (ICML, 2022)

Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability. - Whiyoung Jung; Myungsik Cho; Jongeui Park; Youngchul Sung (NeurIPS, 2022)

A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning. - Woojun Kim; Whiyoung Jung; Myungsik Cho; Youngchul Sung (AAMAS, 2023)

Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition. - Suyoung Lee; Myungsik Cho; Youngchul Sung (NeurIPS, 2023)

Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling. - Myungsik Cho; Jongeui Park; Suyoung Lee; Youngchul Sung (ICML, 2024)