Abstract: For the deployment of battlefield command resources in a distributed environment, there are problems such as low efficiency, slow speed, failure to reach the expected strategy, and excessive data set, resulting in excessive computing resource loss, a multi-agent alliance command in distributed environment is proposed. The control resource deployment optimization algorithm, by improving the learning rate of the gradient descent algorithm in deep learning, changing the originally set learning rate to adaptive learning rate, and then designing a multi-agent alliance for command and control resource deployment. It proves that the algorithm has superior adaptability to this problem, and can effectively solve the problem of command and control resource deployment optimization of multi-agent alliance in the distributed environment.

Key words: deep learning, distributed multi-agent, resource deployment optimization, gradient descent algorithm, agent alliance