Resource constrained deep reinforcement learning
In urban environments, resources have to be constantly matched to the “right” locations where customer demand is present. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in ERS (Emergency Response Systems); vehicles (cars,...
Saved in:
Main Authors: | BHATIA, Abhinav, VARAKANTHAM, Pradeep, KUMAR, Akshat |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2019
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5059 https://ink.library.smu.edu.sg/context/sis_research/article/6062/viewcontent/3528_Article_Text_6577_1_10_20190619.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Multiagent decision making and learning in urban environments
by: KUMAR, Akshat
Published: (2019) -
An efficient algorithm for learning event-recording automata
by: LIN, Shang-Wei, et al.
Published: (2011) -
Automatic code review by learning the revision of source code
by: SHI, Shu-Ting, et al.
Published: (2019) -
Learning program semantics with code representations: An empirical study
by: SIOW, Jing Kai, et al.
Published: (2022) -
Knowledge compilation for constrained combinatorial action spaces in reinforcement learning
by: LING, Jiajing, et al.
Published: (2023)