基于虚拟安全笼的公路自动驾驶弱监督强化学习
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
摘要：
神经网络和强化学习的使用在自主车辆控制中变得越来越流行。然而，由此产生的控制策略的不透明性对在自主车辆中部署基于神经网络的控制提出了重大障碍。在本文中，我们提出了一种基于强化学习的方法，自主车辆纵向控制，其中基于规则的安全笼提供增强的安全性的车辆以及弱监督的强化学习代理。通过将代理引导到有意义的状态和动作，这种弱监督改善了训练过程中的收敛性，并增强了最终训练策略的安全性。这种基于规则的监督控制器具有完全可解释的进一步优势，从而使传统的验证和验证方法能够确保车辆的安全性。我们比较了有和没有安全笼的模型，以及具有最佳和约束模型参数的模型，并表明弱监督始终提高了探索的安全性，收敛速度和模型性能。此外，我们表明，当模型参数受到约束或次优时，安全笼可以使模型学习安全驾驶策略，即使模型无法单独通过强化学习进行训练。
英文摘要：
The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement learning based approach to autonomous vehicle longitudinal control, where the rule-based safety cages provide enhanced safety for the vehicle as well as weak supervision to the reinforcement learning agent. By guiding the agent to meaningful states and actions, this weak supervision improves the convergence during training and enhances the safety of the final trained policy. This rule-based supervisory controller has the further advantage of being fully interpretable, thereby enabling traditional validation and verification approaches to ensure the safety of the vehicle. We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance. Additionally, we show that when the model parameters are constrained or sub-optimal, the safety cages can enable a model to learn a safe driving policy even when the model could not be trained to drive through reinforcement learning alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
