Simulating multi-exit evacuation using deep reinforcement learning

2020-07-11 · Dong Xu, Xiao Huang, Joseph Mango, Xiang Li, Zhenlong Li

Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with massive pedestrians. We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability, and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: 1) varying pedestrian distribution ratios, 2) varying exit width ratios, and 3) varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to the high efficiency of exit utilization.

📄 PDF Abstract BibTeX arXiv:2007.05783

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Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

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DQN A DQN, or Deep Q-Network, approximates a state-value function in a Q-Learning framework with a neural network. In the Atari…

Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles

2020-11-30 · Yihao Zhang, Zhaojie Chai, George Lykotrafitis

A very successful model for simulating emergency evacuation is the social-force model. At the heart of the model is the self-driven force that is applied to an agent and is directed towards the exit. However, it is not c…

Deep Reinforcement Learning Q-Learning

Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment

2019-05-23 · Jivitesh Sharma, Per-Arne Andersen, Ole-Chrisoffer Granmo, Morten Goodwin

We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a complex task which is difficult to solve wit…

OpenAI Gym Q-Learning reinforcement-learning Reinforcement Learning+2

reinforcement-learning Reinforcement Learning

Simulating multi-exit evacuation using deep reinforcement learning

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