Reinforcement learning for collective multi-agent decision making
In this thesis, we study reinforcement learning algorithms to collectively optimize decentralized policy in a large population of autonomous agents. We notice one of the main bottlenecks in large multi-agent system is the size of the joint trajectory of agents which quickly increases with the number...
Saved in:
Main Author: | NGUYEN, Duc Thien |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2018
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/etd_coll/162 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1162&context=etd_coll |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Integrating motivated learning and k-winner-take-all to coordinate multi-agent reinforcement learning
by: TENG, Teck-Hou, et al.
Published: (2014) -
Towards explaining sequences of actions in multi-agent deep reinforcement learning models
by: KHAING, Phyo Wai, et al.
Published: (2023) -
Effective reinforcement learning for collaborative multi-agent domains
by: LAU QIANGFENG PETER
Published: (2013) -
HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning
by: GENG, Minghong, et al.
Published: (2024) -
End-to-end deep reinforcement learning for multi-agent collaborative exploration
by: CHEN, Zichen, et al.
Published: (2019)