A Task-driven Group Cooperative Crowd Simulation Framework via Multi Agent Reinforcement Learning

In recent years, the research has been ongoing about crowd simulation. In many situations, crowd are usually organized into several groups and proceed to their respective target location, to accomplish something or perform a task together for a duration, and this process may involve communication and cooperation within the group. We define such crowd as a task-driven group cooperative crowd, and focus on the crowd movement and group behavior during this process. The movement process of the crowd involves multiple stages and exhibits different behaviors at different stage. Within this framework, we consider a problem scenario where crowd within the group orderly gathering in front of objects for a duration to perform a task. We propose a data-driven framework based on Multi-Agent Reinforcement Learning(MARL), including task parameter policy learning and curriculum training strategy. We use a set of task parameters to describe the current task information and integrate them into the iterative optimization process of the policy. Based on this, we design agent observation and a weighted reward function, to make the agent recognize the current task stage and learn the corresponding behaviors. Utilizing curriculum training strategy, the training process is divided into multi steps, gradually increasing the training complexity. We conduct a series of experiments to verify the effectiveness and generalization of the framework and design a museum exhibition scenario to verify the practicality. We provide a demo to supplement the more details, https://github.com/ICSRC/2024ICVRV.