Abstract
Reinforcement Learning knows an important success in various applications such as robotics, games, resource management, etc. However, it is proving insufficient to solve the problem of crowd evacuation, in a realistic environment because the crowd situation is very dynamic, with many changing variables and complex constraints that make it difficult to solve. And there is no standard reference environment that can be used to train agents in an evacuation. A realistic environment can be complex to design. In this paper, we use Deep Reinforcement Learning to train agents in evacuation planning. The environment is modeled as a grid with obstacles and the solution is modeled using intelligent agents. It takes into account certain parameters, such as the number of occupants, the capacity level, and the time to pass through the exit doors. The objective is to allow the evacuation of the occupants as quickly as possible and to help the agent decide on the optimal escape route under varying conditions over time. And subsequently, this approach will be useful to evacuation decision makers to better implement dynamic arrow signs. The results are motivating to use this type of learning to optimize decision support in evacuation situations.
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Abdallah, W., Kanzari, D., Madani, K. (2021). Towards a Deep Reinforcement Approach for Crowd Flow Management. In: Rojas, I., Joya, G., Català , A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_35
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DOI: https://doi.org/10.1007/978-3-030-85030-2_35
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