The invention relates to the technical field of intelligent
vehicle control, in particular to a multi-agent
model parameter initialization method and device based on
imitation learning and a storage medium, and the method comprises the steps: S1, constructing an
imitation learning framework based on a single-vehicle expert strategy, training a single-agent strategy network through employing an iterative
online strategy learning normal form, and obtaining a single-agent model; obtaining an initial strategy
network model with basic driving ability; s2, a controlled random expert
distillation mechanism is introduced in the training process, augmented state vectors aligned with the multi-agent environment are constructed through virtual other vehicle state injection, expert action tendency distribution is dynamically adjusted through a strategy focusing factor, and enhanced pre-training strategy network parameters are obtained; and S3,
copying the enhanced pre-training strategy network parameters and initializing each agent strategy network in the multi-agent
reinforcement learning environment. According to the method, the problems of
slow convergence and easy
local optimum in a multi-vehicle cooperative task are solved,
hot start is realized through
imitation learning, and the training efficiency and the final performance are improved.