Crowd evacuation simulation method and system based on deep reinforcement learning

Abstract

The invention discloses a crowd evacuation simulation method and system based on deep reinforcement learning. The method comprises the steps of initializing a constructed evacuation scene simulation model according to scene information and crowd parameter information; grouping crowds, and dividing leaders and followers of each group; adopting a hierarchical path planning method to obtain evacuation paths of crowds, wherein a leader in an upper-layer group performs global path planning through an EMADDPG algorithm to obtain an optimal evacuation path, and followers in a lower-layer group avoidobstacles and follow the leader to evacuate along the optimal evacuation path. A learning curve and a high-priority experience playback strategy are introduced on the basis of a traditional MADDPG algorithm, an EMADDPG algorithm is formed, the learning efficiency of the algorithm is improved, a hierarchical path planning method is provided on the basis of the EMADDPG algorithm and used for planning evacuation paths of crowds, the path planning time is effectively shortened, and the crowd evacuation efficiency is improved. People can be better guided to evacuate, and the crowd evacuation efficiency is improved.

Description

technical field [0001] The present disclosure relates to a crowd evacuation simulation method and system based on deep reinforcement learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] With the increasing frequency of public safety issues, large-scale crowd evacuation has become an important link that cannot be ignored in emergency response. In crowded places, once a dangerous accident occurs, the crowd will rush to escape the scene in order to avoid the danger, which will cause crowding during the evacuation process. Failure to evacuate in time may even cause collisions and stampede accidents, causing secondary damage to the evacuated crowd. At the same time, large-scale crowd evacuation is a complex process, and large-scale crowd evacuation experiments are difficult to carry out due to problems such as organizational difficultie...

AI Technical Summary

Problems solved by technology

The multi-agent deep deterministic policy gradient (Multi-Agent Deep Deterministic Policy Gradient, MADDPG) algorithm proposed by Lowe et al. is a new multi-agent deep reinforcement learning algorithm, but the algorithm also has immutable state space and experience playback. Random and other problems seriously affect the learning efficiency of the algorithm

At the same time, with the increase in the number of agents guiding evacuation and the increase in the complexity of the environment, a huge state space is inevitably brought about. These problems seriously affect the application effect of the algorithm in the field of crowd evacuation.

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