摘要
To avoid crowd evacuation simulations depending on 2D environments and real data,we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning(DRL)and 3D physical environments(3DPEs).In 3DPEs,we construct simulation scenarios from the aspects of geometry,semantics and physics,which include the environment,the agents and their interactions,and provide training samples for DRL.In DRL,we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy.With a unified configuration,we conduct evacuation simulations.In scenarios with one exit,we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics(number,mass and height)on evacuation.In scenarios with two exits and a uniform(nonuniform)distribution of agents,we explore the impact of exit characteristics(width and relative position)and agent characteristics(height,initial location and distribution)on agent exit selection and evacuation.Overall,interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation.
基金
supported and funded by the National Key Technology R&D Program of China[grant number 2020YFC0833103]
the Pilot Fund of Frontier Science and Disruptive Technology of Aerospace Information Research Institute,Chinese Academy of Sciences[grant number E0Z211010F]
the National Natural Science Foundation of China[grant number 41971361 and the National Natural Science Foundation of China[grant number 42171113].
