Qin Yang's repositories
Self-Adaptive-Swarm-System_SASS_MRS2019
Self-Adaptive_Swarm_System(SASS) for 2019 IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS) Version. Paper: Self-Reactive Planning of Multi-Robots with Dynamic Task Assignments
Bayesian-Soft-Actor-Critic
Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system utility, decrease the overall cost, and increase mission success probability. Deep Reinforcement Learning (DRL) helps organize agents' behaviors and actions based on their state and represents complex strategies (composition of actions). This research introduces a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN).
Needs-driven-MRS-Cooperation-SSRR2020
This is the original code and data for 2020 The IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). Paper: Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions
Gut-Pursuit-Domain-in-Robotarium-ISR2022
"Game-theoretic utility tree for multi-robot cooperative pursuit strategy" Paper with Code for 2022 the 54th international symposium on robotics (ISR europe). IEEE. Furthermore, include the codes of Explore Domain implementing in Robotarium.
QinYang-PhD-Dissertation-SASS
Multi-agent systems (MAS) could play a pivotal role in realizing future intelligent workspaces, especially in building so-called artificial social systems, such as self-driving cars and multi-robot systems (MRS). For example, MAS/MRS cooperates to increase mission performance in many applications, including exploration, surveillance, defense, humanitarian, and emergency missions like urban search and rescue.
RickYang2016.github.io
Qin Yang Personal Website
SASS_SMC2020
This version is the original code and data for the 2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS. Paper: Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System
Game-theortic-Utility-Tree--SAC2023-IRMAS
It is a new Game-Theoretic Utility Network called Game-Theoretic Utility Tree (GUT) helping agents making-decision and adapting to adversarial environments, which is also based on the Self-Adaptive Swarm System (SASS).
Operating-System
Some practice code about OS
RNE-Agent-Trust-Model-SMC2021
This version is the original code and data for the 2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS. Paper: How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model
Self-Adaptive-Swarm-System-SASS-Unity-Version
This project is built a general simulation architecture for the Self-Adaptive Swarm System (SASS) with Unity.