Deep Temporal Interpolation of Radar-based Precipitation
Michiaki Tatsubori, Takao Moriyama, et al.
ICASSP 2022
Reinforcement learning has led to considerable break-throughs in diverse areas such as robotics, games and many others, but its application in complex real-world decision making problems remains limited. Many problems in OM are characterized by large action spaces and stochastic system dynamics, providing a challenge for existing RL methods that rely on enumeration techniques to solve per step action problems. To resolve these issues, we develop Programmable Actor Reinforcement Learning (PARL), a policy iteration method that uses techniques from integer programming and sample average approximation. We demonstrate its effectiveness on a variety of multi-echelon inventory management settings.
Michiaki Tatsubori, Takao Moriyama, et al.
ICASSP 2022
Dhaval Salwala, Seshu Tirupathi, et al.
Big Data 2022
Ademir Ferreira Da Silva, Levente Klein, et al.
INFORMS 2022
Pavithra Harsha, Ali Koc, et al.
INFORMS 2021