United States Department of Transportation
i
Multi-Agent Reinforcement Learning-Based Evacuation Models under Emergency
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2022-01-31
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[PDF-718.64 KB]
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Edition:Final Report, 05/01/2019-12/31/2021
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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.
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