论文标题
通过不确定性估计，通过强化学习在自主驾驶中的战术决策
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
论文作者
论文摘要
强化学习（RL）可用于创建用于自动驾驶的战术决策代理。但是，以前的方法仅执行输出决策，而没有提供有关代理商对建议行动的信心的信息。本文研究了基于具有其他随机先验功能（RPF）的神经网络集合的贝叶斯RL技术如何用于估计自动驾驶中决策的不确定性。还引入了一种分类是否应视为安全的方法。通过在高速公路驾驶方案上训练代理商来评估合奏RPF方法的性能。结果表明，训练有素的代理可以估计其决策的不确定性，并表明当代理面临远离培训分配的情况时，不可接受的水平。此外，在训练分布中，集合RPF代理的表现优于标准的深Q网络代理。在这项研究中，估计的不确定性用于在未知情况下选择安全的行动。但是，不确定性信息也可以用于识别应添加到培训过程中的情况。
Reinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. However, previous approaches only output decisions and do not provide information about the agent's confidence in the recommended actions. This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving. A method for classifying whether or not an action should be considered safe is also introduced. The performance of the ensemble RPF method is evaluated by training an agent on a highway driving scenario. It is shown that the trained agent can estimate the uncertainty of its decisions and indicate an unacceptable level when the agent faces a situation that is far from the training distribution. Furthermore, within the training distribution, the ensemble RPF agent outperforms a standard Deep Q-Network agent. In this study, the estimated uncertainty is used to choose safe actions in unknown situations. However, the uncertainty information could also be used to identify situations that should be added to the training process.
