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Younggyo Seo
Younggyo Seo is a member of technical staff at Amazon working on reinforcement learning, robotics, and foundation models.
Montenegro
Categories
- Marketing & Creative (primary)
Service areas
- Montenegro
Public contacts
- Public phone: 2505.22642
- github: https://github.com/younggyoseo
- github: https://github.com/amazon-far/holosoma
- github: https://github.com/younggyoseo/FastTD3
- github: https://github.com/younggyoseo/CQN-AS
- github: https://github.com/huiwon-jang/CoordTok
- github: https://github.com/younggyoseo/CQN
- github: https://github.com/chernyadev/bigym
- github: https://github.com/carlosferrazza/M3L
- github: https://github.com/kingdy2002/VCSE
- github: https://github.com/csmile-1006/ARP
- github: https://github.com/younggyoseo/MV-MWM
- github: https://github.com/ademiadeniji/lamp
- github: https://github.com/younggyoseo/MWM
- github: https://github.com/younggyoseo/apv
- github: https://github.com/alinlab/SURF
- github: https://github.com/alinlab/oreo
- github: https://github.com/junsu-kim97/HIGL
- github: https://github.com/shlee94/Off2OnRL
- github: https://github.com/younggyoseo/re3
- github: https://github.com/younggyoseo/trajectory_mcl
Directory status
Registry verification: Not Registry Verified
Directory score: 74
Public source evidence
- Younggyo Seo
- Learning Sim-to-Real Humanoid Locomotion in 15 Minutes
- Younggyo Seo
- FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control
- Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning
- Continuous Control with Coarse-to-fine Reinforcement Learning
- Publications — Younggyo Seo