基于强化学习的三维游戏控制算法

基于强化学习，设计了一个面向三维第一人称射击游戏(DOOM)的智能体，该智能体可在游戏环境下移动、射击敌人、收集物品等.本文算法结合深度学习的目标识别算法Faster RCNN与Deep Q-Networks(DQN)算法，可将DQN算法的搜索空间大大减小，从而极大提升本文算法的训练效率.在虚拟游戏平台(ViZDoom)的两个场景下(Defend_the_center和Health_gathering)进行实验，将本文算法与最新的三维射击游戏智能体算法进行比较，结果表明本文算法可以用更少的迭代次数实现更优的训练结果.

Control Algorithm of Three-Dimensional Game Based on Reinforcement Learning

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.