Abstract: Based on reinforcement learning， an agent for three-dimensional first person shooting game(DOOM)was designed. The agent can move， shoot enemies and collect objects in the game environment. The proposed algorithm combines the Faster RCNN algorithm of deep learning and the Deep Q-Networks(DQN)algorithm of reinforcement learning， which can greatly reduce the search space of DQN algorithm and improve the training efficiency of the proposed algorithm. The experiments were carried out in two scenes(Defend_the_center and Health_gathering)of the virtual game platform(ViZDoom)， and the proposed algorithm was compared with the state-of-the-art three-dimensional shooting game agent algorithm. The results show that the proposed algorithm can achieve better training results with fewer iterations.

Key words: reinforcement learning; deep learning; object detection; Faster RCNN; Deep Q-Networks(DQN)