This paper investigates how a Bayesian reinforcement learning method can be
used to create a tactical decision-making agent for autonomous driving in an
intersection scenario, where the agent can estimate the confidence of its
recommended actions. An ensemble of neural networks, with additional randomized
prior functions (RPF), are trained by using a bootstrapped experience replay
memory. The coefficient of variation in the estimated $Q$-values of the
ensemble members is used to approximate the uncertainty, and a criterion that
determines if the agent is sufficiently confident to make a particular decision
is introduced. The performance of the ensemble RPF method is evaluated in an
intersection scenario, and compared to a standard Deep Q-Network method. It is
shown that the trained ensemble RPF agent can detect cases with high
uncertainty, both in situations that are far from the training distribution,
and in situations that seldom occur within the training distribution. In this
study, the uncertainty information is used to choose safe actions in unknown
situations, which removes all collisions from within the training distribution,
and most collisions outside of the distribution.

本研究探讨了如何使用贝叶斯强化学习方法和散置神经网络来解决交叉路口自主驾驶情景下的决策问题，训练出的系统可以估计可靠性并识别未知情境中的危险，其性能远超标准的基于深度 Q 网络的决策系统。