Name
Title | ||
|---|---|---|
Decentralized Optimal Tracking Control for Large-scale Multi-Agent Systems under Complex Environment: A Constrained Mean Field Game with Reinforcement Learning Approach |
Abstract | ||
|---|---|---|
In this paper, the optimal tracking control for large-scale multi-agent systems (MAS) under constraints has been investigated. The Mean Field Game (MFG) theory is an emerging technique to solve the “curse of dimensionality” problem in large-scale multi-agent decision-making problems. Specifically, the MFG theory can calculate the optimal strategy based on one unified fix-dimension probability dens... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CCTA48906.2021.9658641 | 2021 IEEE Conference on Control Technology and Applications (CCTA) |
Keywords | DocType | ISBN |
Optimal control,Games,Artificial neural networks,Reinforcement learning,Aerospace electronics,Probability density function,Approximation algorithms | Conference | 978-1-6654-3643-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zejian Zhou | 1 | 2 | 3.42 |
| Hao Xu | 2 | 0 | 0.68 |