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In this paper, we propose a method to adapt the exploration ratio in multi-agent reinforcement learning. The adaptation of exploration ratio is important in multi-agent learning, as this is one of key parameters that affect the learning performance. In our observation, the adaptation method can adjust the exploration ratio suitably (but not optimally) according to the characteristics of environments. We investigated the evolutionarily adaptation of the exploration ratio in multi-agent learning. We conducted several experiments to adapt the exploration ratio in a simple evolutionary way, namely, mimicking advantageous exploration ratio (MAER), and confirmed that MAER always acquires relatively lower exploration ratio than the optimal value for the change ratio of the environments. In this paper, we propose a second evolutionary adaptation method, namely, win or update exploration ratio (WoUE). The results of the experiments showed that WoUE can acquire a more suitable exploration ratio than MAER, and the obtained ratio was near-optimal.