Aiming at the problems such as poor"learning to learn"ability of the autonomous driving model based on deep reinforcement learning,start training from scratch when facing new driving tasks,slow training speed,poor generaliza-tion performance and so on,this paper proposes a MPPO(Meta-PPO)autonomous driving model based on meta-rein-forcement learning.The MPPO model combines the meta-learning with the reinforcement learning,and uses the meta-learn-ing algorithm to train a set of good parameters for the autonomous driving model in the meta-training stage,so that the model can quickly reach the convergence state after a small amount of sample fine-tuning on the basis of this set of pa-rameters when facing new driving tasks.The experimental results show that,in the navigation scenario task,compared with the benchmark autonomous driving model based on reinforcement learning,the convergence speed of MPPO model in-creases 2.52 times,the reward value increases 7.50％,the offset reduces 7.27％,and the generalization performance also improves to a certain extent.
维普
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