A Rigorous Risk-aware Linear Approach to Extended Markov Ratio Decision Processes with Embedded Learning
- Alexander Zadorojniy
- Takayuki Osogami
- et al.
- IJCAI 2023
Takayuki Osogami is a senior technical staff member at IBM Research - Tokyo. He is currently leading global research projects on reinforcement learning, industrial applications of reinforcement learning, automation of decision optimization with reinforcement learning, multi-agent reinforcement learning, and integration of reinforcement learning and game theory. He was a group leader of a governmental project supported by Core Research for Evolutionary Science and Technology (CREST), Japan Science and Technology Agency (JST) during 2013-2019, where he developed and applied theory of sequential decision making, human behavior modeling, and neuromorphic computing. He received his Ph.D. in Computer Science from Carnegie Mellon University in August 2005, and a B.Eng. degree in Electronic Engineering from the University of Tokyo in 1998.
See https://sites.google.com/site/takayukiosogami/ for more details.