Reinforcement learning for safe evacuation time of fire in Hong Kong-Zhuhai-Macau immersed tube tunnel

In this paper, authors mainly study the laws of safe evacuation time based on reinforcement learning when fire breaks out in the immersed tunnel. In case of fire, time is life. When the people in the tunnel begin to escape, they will instinctively choose the best path they believed in. It is bound to cause congestion and increase the overall escape time. Therefore, the authors designed the reinforcement learning (RL) scheme with multiple escape routes to seek the Nash equilibrium. In each iteration, they update their escape strategy on the basis of the previous outcome. Since the minimum overall time is the objective function, the result tends to converge. In this paper, the author carried out a fire test with a heat release rate of 50 WM. Therefore, total number of people trapped in the high-temperature hazardous area under the condition of traffic jams is 158. Finally, the minimum safe evacuation time of personnel is calculated as 110.5 s through the reinforcement learning model. This paper will provide scientific support for long offshore immersed tube tunnel fire evacuation and emergency evacuation decision-making system.