Multi-robot task planning method based on deep reinforcement learning and regional balance

The invention provides a multi-robot task planning method based on deep reinforcement learning and regional balance. The method comprises the following steps: generating a sample data set for training a single-robot welding path planning model, and constructing a single-robot welding path planning model based on a deep neural network; training the single-robot welding path planning model by using the training sample data set and reinforcement learning to obtain a trained single-robot welding path planning model; dividing the welding task area into a plurality of welding sub-areas by using an area balance method, and distributing the welding sub-areas to a plurality of welding robots; each welding robot obtains a welding task planning result of the responsible welding sub-area through the trained single-robot welding path planning model according to the responsible welding sub-area; and each welding robot carries out welding operation on the responsible welding sub-area according to the welding task planning r