Evolutionary reinforcement learning algorithm for large-scale multi-agent cooperation and confrontation applications

Multi-agent cooperation and confrontation technology have achieved rapid development in recent years. Most extant multi-agent reinforcement learning algorithms simplify the problem by using shared weights or local observation, and are only suitable for scenarios with less than ten agents. Given this, large-scale scene research needs to explore new directions. This paper presents a large-scale multi-agent evolutionary reinforcement jointed method. The multi-agent learning task is separated into numerous stages based on the agent’s scale, and the self-attention mechanism is utilized to handle changing numbers of agents in each step. Simultaneously, to avoid the agents’ poor adaptability in previous stages, the best individuals in the population are chosen at each stage of training via evolutionary techniques. Two typical unmanned aerial vehicle cluster missions, multi-domain joint sea crossing and landing missions, were created to validate the performance of the suggested technique, and the operational rules and reward functions were also given. Experiments have shown that the model trained using the suggested method has good performance and stability and can provide a multi-agent collaborative decision-making model suitable for large-scale environments.