Adversarial Reinforcement Learning for Enhanced Decision-Making of Evacuation Guidance Robots in Intelligent Fire Scenarios
Bibliographic Data
| ID | 22107237 |
|---|---|
| Authors | Hantao Zhao (0000-0003-0398-3842, Southeast University), Zhihao Liang (0009-0001-6826-8420, Southeast University), Tianxing Ma (0000-0002-9887-4843, Chinese Academy of Sciences), Xiaomeng Shi (0009-0007-7072-5110, Southeast University), Mubbasir Kapadia (0000-0002-3501-0028, Rutgers, the State University of New Jersey), Tyler Thrash (0000-0002-3011-7029, Saint Louis University), Christoph Hölscher (0000-0002-5536-6582, ETH Zurich), Jinyuan Jia (0000-0002-7772-4766, The Hong Kong University of Science and Technology (Guangzhou)), Bo Liu (0000-0002-6536-517X, Purple Mountain Laboratories), Jiuxin Cao (0000-0002-2448-6717, Southeast University) |
| Year | 2025 |
| Volume | 12 |
| Issue | 5 |
| Pages | 2030-2046 |
| Publication date | 2025-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3502420 |
| OpenAlex | W4405303977 |
| Language | EN |
| References cited | 48 |
In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in complex buildings and incorporates robotic navigation and intelligent path planning. A novel simulated human behavior model was integrated, capable of complex human–robot interaction, independent escape route searching, and exhibiting herd mentality and memory mechanisms. We also proposed a multiagent framework that combines MARL and ARL to enhance overall evacuation efficiency and robustness. Additionally, we developed a new ARL evaluation framework that provides a novel method for quantifying agents’ performance. Various experiments of differing difficulty levels were conducted, and the results demonstrate that the proposed framework exhibits advantages in emergency evacuation scenarios. Specifically, our ARLR approach increased survival rates by 1.8% points in low-difficulty evacuation tasks compared to the RLR approach using only MARL algorithms. In high-difficulty evacuation tasks, the ARLR approach raised survival rates from 46.7% without robots to 64.4%, exceeding the RLR approach by 1.7% points. This study aims to enhance the efficiency and safety of human–robot collaborative fire evacuations and provides theoretical support for evaluating and improving the performance and robustness of ARL agents
Adversarial system · Computer security · Reinforcement learning · Robot · Computer Science · Engineering · Evacuation and Crowd Dynamics · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |