A Model-Free Solution for Stackelberg Games Using Reinforcement Learning and Projection Approaches-Reference-Cited by-同舟云学术

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A Model-Free Solution for Stackelberg Games Using Reinforcement Learning and Projection Approaches

Published:2024-06-20 Issue: Volume: Page:1-7
ISSN:
Container-title:2024 IEEE International Symposium on Robotic and Sensors Environments (ROSE)
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Author:

Abouheaf Mohammed¹,Gueaieb Wail²,Miah Suruz³,Abdelhameed Esam H.⁴

Affiliation:

1. Robotics Engineering, Bowling Green State University,Bowling Green,OH,USA,43403

2. School of Electrical Engineering and Computer Science, University of Ottawa,Ottawa,Ontario,Canada,K1N 6N5

3. Bradley University,Department of Electrical and Computer Engineering,Peoria,Illinois,USA,61615

4. Aswan University,Faculty of Energy Engineering,Aswan,Egypt,81528

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10590708/10590795/10590850.pdf?arnumber=10590850

Reference45 articles.

1. Stochastic Strategies for Robotic Surveillance as Stackelberg Games

2. Cooperative Control of Mobile Robots with Stackelberg Learning

3. Stackelberg Strategic Guidance for Heterogeneous Robots Collaboration

4. Task-Driven Cooperative Internet of the Robotic Things Crowdsourcing: From the Perspective of Hierarchical Game-Theoretic

5. Decentralized approximated optimal control for modular robot manipulations with physical human–robot interaction: a cooperative game-based strategy

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