| Popis: |
Deep Q Network (DQN) takes the entire game interface as input, makes use of neural network to output Q value, and maps it into actions. However, the contribution of different game interfaces to Q value often varies, and sometimes only a few interfaces are closely related to the execution of agents. Hence, we propose a deep reinforcement learning model based on multi-experience pool local state parallel Q-Network (MEPLSPQ-Network), which takes the advantage of multiple parallel Q networks to predict Q values collaboratively. In this model, the input of each Q network is the non-overlapping sub-region of the original game interface, and subsequently each Q network will study respectively what characteristics different sub-regions of the game interface have. Experimental results indicate that the performance of MEPLSPQ-Network exceeds that of DQN in three various game scenes. |
