关键词: 多动机引导, Q学习, Sarsa学习, 无人坦克, 对战仿真

Abstract: To improve the classic reinforcement learning, through the introduction of motivation, prior knowledge is introduced, and the learning speed is speeded up. As to the iteration strategy, it adopts “on-policy” iterative Sarsa learning algorithm instead of traditional “off-policy” Q learning algorithm. It proposes Multi-Motivation Sarsa learning algorithm（MMSarsa） and respectively carries out the comparative tests on tank battle simulation with Q-learning algorithm and Sarsa learning algorithm. The results of experiment show that Sarsa learning algorithm based on motivation guidance has fast convergence rate and high learning efficiency.

Key words: multi-motivation guidance, Q learning, Sarsa learning, unmanned tank, battle simulation