Scaling up cooperative multi-agent reinforcement learning systems
Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems, wherein both the number of agents and the le...
Saved in:
Main Author: | GENG, Minghong |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9750 https://ink.library.smu.edu.sg/context/sis_research/article/10750/viewcontent/p2737__1_.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Benchmarking MARL on long horizon sequential multi-objective tasks
by: GENG, Minghong, et al.
Published: (2024) -
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) -
Distributed relational temporal difference learning
by: Lau, Q.P., et al.
Published: (2014) -
HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning
by: GENG, Minghong, et al.
Published: (2024)