Background-Suicide is the most common cause of death among youth in China.

Traditional local motion simulation focuses largely on avoiding collisions in the next frame. However, due to its lack of forward looking, the global trajectory of agents usually seems unreasonable. As a method of optimizing the overall reward, deep reinforcement learning (DRL) can better correct the problems that exist in the traditional local motion simulation method. In this article, we propose a local motion simulation method integrating optimal reciprocal collision avoidance (ORCA) and DRL, referred to as ORCA‐DRL. The main idea of ORCA‐DRL is to perform local collision avoidance detection via ORCA and smooth the trajectory at the same time via DRL. We use a deep neural network (DNN) as the state‐to‐action mapping function, where the state information is detected by virtual visual sensors and the action space includes two continuous spaces: speed and direction. To improve data utilization and speed up the training process, we use the proximal policy optimization based on the actor–critic (AC) framework to update the DNN parameters. Three scenes (circle, hallway, and crossing) are designed to evaluate the performance of ORCA‐DRL. The results reveal that, compared with the ORCA, our proposed ORCA‐DRL method can: (a) reduce the total number of frames, leading to less time for agents to reach their destination; and (b) effectively avoid local optima, evidenced by smoothed global trajectories.

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 large numbers of pedestrians. We propose a multi‐exit evacuation simulation based on deep reinforcement learning (DRL), referred to as the MultiExit‐DRL, which involves 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: varying pedestrian distribution ratios; varying exit width ratios; and 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 a high efficiency of exit utilization.