Q-CF multi-Agent reinforcement learning for resource allocation problems

When a multi-Agent reinforcement learning algorithm is used in complex distributed systems,problems such as huge state space and low learning efficiency arise.In this paper,a multi-Agent reinforcement learning algorithm was studied for the resource allocation problem in a network environment.By combining the Q-learning algorithm and the chain feedback learning mechanism,a novel Q-CF multi-Agent reinforcement learning algorithm was presented.In the Q-CF algorithm,multi-Agent cooperation was realized based on the mechanism of information chain feedback.Simulation results show that compared with the multi-Agent Q-learning algorithm in existence,the proposed algorithm in this paper has a faster convergence speed while at the same time ensures the performance optimization of cooperation policy.