Multiagent Collaboration for Emergency Evacuation Using Reinforcement Learning for Transportation Systems

Author:

Yang Yupeng1,Yu Jiahao2,Liu Dahai2ORCID,Lee Sang-A2,Namilae Sirish3,Islam Sabique3,Gou Huaxing1,Park Hyoshin4,Song Houbing5ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, Security and Optimization for Networked Globe Laboratory, Embry–Riddle Aeronautical University, Daytona Beach, FL, USA

2. School of Graduate Studies, Collage of Aviation, Embry–Riddle Aeronautical University, Daytona Beach, FL, USA

3. Department of Aerospace Engineering, Embry–Riddle Aeronautical University, Daytona Beach, FL, USA

4. Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA

5. Department of Electrical Engineering and Computer Science, Embry–Riddle Aeronautical University, Daytona Beach, FL, USA

Funder

Center for Advanced Transportation Mobility (CATM), U.S. Department of Transportation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Pharmacology (medical)

Reference32 articles.

1. Learning to communicate with deep multi-agent reinforcement learning;foerster;arXiv 1605 06676,2016

2. Collaboration of agents via cooperative multi-agent deep reinforcement learning;balachandar;arXiv 1907 00327,2019

3. Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents

4. Reinforcement learning approach for adapting complex agent-based model of evacuation to fast linear model

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