Hierarchical multi-agent reinforcement learning with options
In recent years, there are many state-of-the-art multi-agent reinforcement learning (MARL) algorithms that aim to get multiple agents to work together to achieve a common goal. COMA is one of these breakthroughs that proposes a counterfactual baseline to address the credit assignment problem in mult...
Saved in:
Main Author: | Ang, Wan Qi |
---|---|
Other Authors: | Lana Obraztsova |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/148028 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Data-efficient multi-agent reinforcement learning
by: Wong, Reuben Yuh Sheng
Published: (2022) -
Lying in pursuit evasion task with multi-agent reinforcement learning
by: Cheng, Damien Shiao Kiat
Published: (2022) -
Multi-agent reinforcement learning for complex sequential decision-making
by: Qiu, Wei
Published: (2024) -
Towards coordinated multi-agent exploration problem via segmentation and reinforcement learning
by: Chen, Zichen
Published: (2020) -
Robust multi-agent team behaviors in uncertain environment via reinforcement learning
by: Yan, Kok Hong
Published: (2022)