Swarm reinforcement learning algorithms -exchange of information among multiple agents-

In ordinary reinforcement learning algorithms, a single agent learns to achieve a goal through many episodes. If a learning problem is complicated, it may take much computation time to acquire the optimal policy. Meanwhile, for optimization problems, multi-agent search methods such as particle swarm optimization have been recognized that they are able to find rapidly the global optimal solution for multi-modal functions with wide solution space. We recently proposed swarm reinforcement learning algorithms in which multiple agents learn through not only their respective experiences but also exchanging information among them. In these algorithms, it is important how to design a method of exchanging the information. This paper proposes several methods of exchanging the information. The proposed algorithms using these methods are applied to a shortest path problem, and their performance is compared through numerical experiments.