HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning
Multi-agent deep reinforcement learning (MADRL) has shown remarkable advancements in the past decade. However, most current MADRL models focus on task-specific short-horizon problems involving a small number of agents, limiting their applicability to long-horizon planning in complex environments. Hi...
Saved in:
Main Authors: | GENG, Minghong, PATERIA, Shubham, SUBAGDJA, Budhitama, TAN, Ah-hwee |
---|---|
格式: | text |
語言: | English |
出版: |
Institutional Knowledge at Singapore Management University
2024
|
主題: | |
在線閱讀: | https://ink.library.smu.edu.sg/sis_research/8927 https://ink.library.smu.edu.sg/context/sis_research/article/9930/viewcontent/HiSOMA_sv.pdf |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
機構: | Singapore Management University |
語言: | English |
相似書籍
-
Towards Explaining Sequences of Actions in Multi-Agent Deep Reinforcement Learning Models
由: KHAING, Phyo Wai, et al.
出版: (2023) -
Explaining Sequences of Actions in Multi-agent Deep Reinforcement Learning Models
由: KHAING, Phyo Wai, et al.
出版: (2024) -
Benchmarking MARL on Long Horizon Sequential Multi-Objective Tasks
由: GENG, Minghong, et al.
出版: (2024) -
Scaling up Cooperative Multi-agent Reinforcement Learning Systems
由: GENG, Minghong
出版: (2024) -
Multi-agent reinforcement learning in spatial domain tasks using inter subtask empowerment rewards
由: PATERIA, Shubham, et al.
出版: (2019)