Multi-agent reinforcement learning to optimize the dynamic allocation mechanism of social public resources

Uncertainty in the demand and supply of social public resources leads to increased competition, which increases the complexity and collaboration cost of resource allocation. Therefore, this paper introduces a multi-agent reinforcement learning (MARL) algorithm to optimize the dynamic allocation mechanism of social public resources. Firstly, considering the problem of partial loss of information due to environmental unobservability in a multi-intelligent body environment, a MARL algorithm based on prophet-guided (MADRLPG) is proposed. On this basis, a flexible overlapping organization framework under spatio-temporal constraints is constructed. In addition, a dynamic resource allocation algorithm under spatio-temporal constraints is proposed to solve the unbalanced use of resources; meanwhile, a collaborative strategy generation algorithm is proposed to reduce resource competition. Finally, the simulation results show that the resource utilization rate of the proposed method is 97%, which can effectively alleviate the resource constraint problem.