Random Prior Network for Autonomous Driving Decision-Making Based on Reinforcement Learning

Abstract

At present, autonomous driving decision-making solutions take few elements into account while ignoring the unpredictable nature of driving behavior, which makes it challenging to manage complicated traffic situations. To this end, we present a decision-making architecture in this paper that enhances the existing reinforcement learning methodology by combining the bootstrapped technique and the random prior network (RPN). The RPN can give each learner a neural network with unique weights to avoid the contingency created by the artificially built prior functions, while the Bootstrapped technique can balance out the exploration and exploitation. The ego vehicle was trained by three algorithms and verified in random environments to evaluate the effectiveness of our method. The results show that our algorithm outperformed the current reinforcement learning algorithms.