Multi-Agent Reinforcement Learning-Based Evacuation Models under Emergency

Yang, Yupeng; Yu, Jiahao; Karnati, Kavya; Liu, Dahai; Namilae, Sirish; Park, Hyoshin

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Multi-Agent Reinforcement Learning-Based Evacuation Models under Emergency

2022-01-31
By Yang, Yupeng ; Yu, Jiahao ; Karnati, Kavya

File Language:

English

Details

Creators:

Yang, Yupeng ; Yu, Jiahao ; Karnati, Kavya ; Liu, Dahai ; Namilae, Sirish ; Park, Hyoshin
Corporate Creators:

North Carolina A&T State University. Transportation Institute. Center For Advanced Transportation Mobility ; Embry-Riddle Aeronautical University
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:
Publication/ Report Number:

CATM-2022-R3-ERAU
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report, 05/01/2019-12/31/2021
Corporate Publisher:

North Carolina A&T Sate University. Transportation Institute. Center For Advanced Transportation Mobility
Abstract:

Reinforcement learning (RL) has been widely used in intelligent transportation systems, especially under emergent situations, RL can explore the dangerous environment and make optimal decisions to guide the evacuation process for human beings. Within RL algorithms, a Multi-agent Reinforcement Learning (MARL) an artificial intelligence-based model that enables multiple agents to communicate and respond to emergent situations for a more efficient exploration and evacuation process, especially when the uncertainty level is high. This study simulated robot agents to explore and evacuate from a dynamic environment with or without collaboration using the MARL Q-learning algorithm. The multi-agent collaboration method was found to perform better than the single-agent exploration regarding the evacuation time, death counts, and reward both in the static threats and dynamic threats environment. Results were discussed, and future directions were given in the end.
Format:

PDF
Funding:

69A3551747125
Download URL:

https://rosap.ntl.bts.gov/view/dot/64673/dot_64673_DS1.pdf
File Type:

[PDF - 718.64 KB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:9b4a736fb82c74ce73e5fa720410c51674a04833840673b5cec26048140ef902872ed1ffa7b5cfb2f9860fb3e9c6f62b43777fe0bc95b2662837466ca7d2ac47

File Language:

English

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