Multi-agent Reinforcement Learning Improvement in a Dynamic Environment Using Knowledge Transfer

Cooperative multi-agent systems are being widely used in different domains. Interaction among agents would bring benefits, including reducing operating costs, high scalability, and facilitating parallel processing. These systems are also a good option for handling large-scale, unknown, and dynamic environments. However, learning in these environments has become a very important challenge in various applications. These challenges include the effect of search space size on learning time, inefficient cooperation among agents, and the lack of proper coordination among agents' decisions. Moreover, reinforcement learning algorithms may suffer from long convergence time in these problems. In this paper, a communication framework using knowledge transfer concepts is introduced to address such challenges in the herding problem with large state space. To handle the problems of convergence, knowledge transfer has been utilized that can significantly increase the efficiency of reinforcement learning algorithms. Coordination between the agents is carried out through a head agent in each group of agents and a coordinator agent respectively. The results demonstrate that this framework could indeed enhance the speed of learning and reduce convergence time.