多智能体协同决策仿真平台研究与开发

Reinforcement learning simulation platform can be an interactive and training environment for reinforcement learning. In order to make the simulation platform compatible with the multi-agent reinforcement learning algorithms and meet the needs of simulation in military field, the similar processes in multi-agent reinforcement learning algorithms are refined and a unified interface is designed to embed and verify different types of deep reinforcement learning algorithms on the simulation platform and to optimize the back-end service of the simulation platform to accelerate the training process of the algorithm model. The experimental results show that, by unifing the interface, the simulation platform can be compatible with many different types of multi-agent reinforcement learning algorithms, and the algorithm training efficiency can be significantly improved after the back-end service framework reconstruction and parameter quantization.