改进行为克隆与DDPG的无人驾驶决策模型OA北大核心CSTPCD

The key to driverless technology is that the decision-making level makes accurate instructions based on the input information of the perception link.Reinforcement learning and imitation learning are better suited for complex scenarios than traditional rules.However,the imitation learning represented by behavioral cloning has the problem of composite error,and this paper uses the priority empirical playback algorithm to improve the behavioral cloning to improve the fitting ability of the model to the demo dataset.The original DDPG(deep deterministic policy gradient)algorithm has the problem of low exploration efficiency,and the experience pool separation and random network distillation(RND)technology are used to improve the DDPG algorithm and improve the training efficiency of DDPG algorithm.The improved algorithm is used for joint training to reduce the useless exploration in the early stage of DDPG training.Verified by TORC(the open racing car simulator)simulation platform,the experimental results show that the proposed method can explore more stable road maintenance,speed maintenance and obstacle avoidance ability within the same number of training times.