Conventional control, such as model-based control, is commonly utilized in
autonomous driving due to its efficiency and reliability. However, real-world
autonomous driving contends with a multitude of diverse traffic scenarios that
are challenging for these planning algorithms. Model-free Deep Reinforcement
Learning (DRL) presents a promising avenue in this direction, but learning DRL
control policies that generalize to multiple traffic scenarios is still a
challenge. To address this, we introduce Multi-residual Task Learning (MRTL), a
generic learning framework based on multi-task learning that, for a set of task
scenarios, decomposes the control into nominal components that are effectively
solved by conventional control methods and residual terms which are solved
using learning. We employ MRTL for fleet-level emission reduction in mixed
traffic using autonomous vehicles as a means of system control. By analyzing
the performance of MRTL across nearly 600 signalized intersections and 1200
traffic scenarios, we demonstrate that it emerges as a promising approach to
synergize the strengths of DRL and conventional methods in generalizable
control.

基于多任务学习的多残差任务学习（MRTL）框架在自动驾驶领域中的深度强化学习（DRL）和常规控制方法的结合具有潜力，能够在多样化的交通场景中实现可迁移的控制。