The goal of this study is to introduce a group of agents with diverse strategies in Go. Each agent should be strong enough (e.g., to guide human players) and still each agent have their own strategy for securing territory to win a game. For diversity, we present two variations, static and intrinsic, and evaluate them. The former enables a user to specify an area of interest to secure, e.g., center-intensive or edge-intensive strategies. The latter encourages agents to have their own strategy without user intervention. Both variations formalize their diversity objectives into pseudo rewards to balance the playing strength and diversity, suitable for the reinforcement learning of Gumbel AlphaZero. Experiments on nine-by-nine Go show both objectives yield a group of diverse agents with almost no or small drawback in playing strength depending on parameters.