  Q learning is widely used to simulate the behaviors of generation companies
(GenCos) in an electricity market. However, existing Q learning method usually
requires numerous iterations to converge, which is time-consuming and
inefficient in practice. To enhance the calculation efficiency, a novel Q
learning algorithm improved by dichotomy is proposed in this paper. This method
modifies the update process of the Q table by dichotomizing the state space and
the action space step by step. Simulation results in a repeated Cournot game
show the effectiveness of the proposed algorithm.
