One of the most important issues of power suppliers is to find bidding strategy in day-ahead electricity auction market to maximize their profit. Usually， the strategies used by power suppliers are too complex to model them by standard techniques based on game theory. Q -learning algorithm can find the optimal strategy through agents' experience， which is obtained from interaction of environment directly. This property makes this algorithm well suitable for dealing with the decision-making problems of power suppliers. AMES， a famous open-source experimental platform， which gives researchers full access to implementation. In this work， a program module， which helps the agents make decision through Q -learning algorithm， has been added into the AMES. A five-bus power system is used for case studies. The results show that the economic characteristics of power suppliers can be simulated by the actions of agents who use Q -learning algorithm. Furthermore， under the same conditions， the Q -learning algorithm illustrates the better ability of exploration than VRE learning algorithm.