抄録
It is well known that the learning effects of agents are important on the diffusion process of rumors in social science. Though these processes can be simulated by agent-based simulations, we cannot disregard the learning effects of agents. In this paper, we deal with an agent-based problem including Q-learning, a kind of reinforcement learning. At first, we design action rules and Q values of agents. Agents decide their attitudes by attitudes which neighbor agents express, and then Q values are updated. Finally, by performing and analyzing simulation studies the effect of proposed algorithm is confirmed.
