Stackelberg game-theoretic trajectory guidance for multi-robot systems with koopman operator

Y Zhao, Q Zhu - 2024 IEEE International Conference on …, 2024 - ieeexplore.ieee.org
Guided trajectory planning involves a leader robot strategically directing a follower robot to
collaboratively reach a designated destination. However, this task becomes notably …

Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous Driving

Z Zheng, S Gu - arxiv preprint arxiv:2405.18209, 2024 - arxiv.org
Ensuring safety in MARL, particularly when deploying it in real-world applications such as
autonomous driving, emerges as a critical challenge. To address this challenge, traditional …

Learning and Game-Theoretic Paradigms for Strategic Coordination of Multi-Agent Autonomous Systems

Y Zhao - 2024 - search.proquest.com
Multi-agent systems (MASs) are intelligent systems that include multiple agents interacting to
achieve various tasks. They have demonstrated significant capabilities in complex …

Stackelberg Game-Theoretic Learning for Collaborative Assembly Task Planning

Y Zhao, L Shi, Q Zhu - arxiv preprint arxiv:2404.12570, 2024 - arxiv.org
As assembly tasks grow in complexity, collaboration among multiple robots becomes
essential for task completion. However, centralized task planning has become inadequate …

A Multi-Agent Q-Learning with Value Function Approximation Based on Single-leader Multi-followers Stackelberg Game

C Zhu, W Yu, H Wang - 2023 IEEE 13th International …, 2023 - ieeexplore.ieee.org
Although multi-agent reinforcement learning (MARL) has made significant progress in
dealing with complex tasks, the hypothesis that agents act simultaneously still limits the …

Heterogeneous Robots Collaborative Applications in Manufacturing Systems With Asymmetric Reinforcement Learning

L Shi - 2023 - search.proquest.com
Smart factories have emerged as a transformative approach to manufacturing, integrating
advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and …