Extending World Models for Multi-Agent Reinforcement Learning in MALMÖ. - Valliappa Chockalingam; Tegg Tae Kyong Sung; Feryal M. P. Behbahani; Rishab Gargeya; Amlesh Sivanantham; Aleksandra Malysheva (AIIDE Workshops, 2018)

HLS-based 360 VR using spatial segmented adaptive streaming. - Hyeon Su Kim; Sang Bum Nam; Sang Geun Choi; Chang Hyung Kim; Tegg Tae Kyong Sung; Chae-Bong Sohn (ICCE, 2018)

Deep Multi-Agent Reinforcement Learning with Relevance Graphs. - Aleksandra Malysheva; Tegg Tae Kyong Sung; Chae-Bong Sohn; Daniel Kudenko; Aleksei Shpilman (CoRR, 2018)

Neural Heterogeneous Scheduler. - Tegg Tae Kyong Sung; Valliappa Chockalingam; Alex Yahja; Bo Ryu (CoRR, 2019)

DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip Resource Scheduling. - Tegg Tae Kyong Sung; Jeongsoo Ha; Jeewoo Kim; Alex Yahja; Bo Ryu (CoRR, 2020)

SoCRATES: System-on-Chip Resource Adaptive Scheduling using Deep Reinforcement Learning. - Tegg Tae Kyong Sung; Bo Ryu (ICMLA, 2021)

A Scalable and Reproducible System-on-Chip Simulation for Reinforcement Learning. - Tegg Tae Kyong Sung; Bo Ryu (CoRR, 2021)

Deep Reinforcement Learning for System-on-Chip: Myths and Realities. - Tegg Tae Kyong Sung; Bo Ryu (IEEE Access, 2022)