The application belongs to the technical field of non-electric variable regulation, and relates to a multi-
robot cooperative path planning and dynamic
obstacle avoidance decision-making method based on deep
reinforcement learning, which comprises the following steps: acquiring a
pose deviation vector, a relative obstacle distance and a transient acceleration vector of a controlled
robot, and calculating an interactive conflict degree feature; using a deep
reinforcement learning model to extract mapping correlation of the above parameters in a multi-
machine coupling environment, and outputting a feedback
gain correction coefficient; establishing a
pose closed-loop regulation loop and mapping the feedback
gain correction coefficient to the loop in real time; adjusting a differential item feedback
gain to constrain a
control signal output gradient, and inhibiting non-continuous steps of a controlled variable; and driving a motor adjustment unit to move the
robot according to the corrected
control signal, wherein the application constructs a regulation architecture based on a dynamic virtual damping, absorbs non-continuous energy jumps generated by path re-planning by using
gain correction, eliminates regulation oscillation induced by instruction steps, keeps a
pose evolution curve stable, and improves motion stability.