Deep Reinforcement Learning (DRL) has shown remarkable success in solving
complex tasks across various research fields. However, transferring DRL agents
to the real world is still challenging due to the significant discrepancies
between simulation and reality. To address this issue, we propose a robust DRL
framework that leverages platform-dependent perception modules to extract
task-relevant information and train a lane-following and overtaking agent in
simulation. This framework facilitates the seamless transfer of the DRL agent
to new simulated environments and the real world with minimal effort. We
evaluate the performance of the agent in various driving scenarios in both
simulation and the real world, and compare it to human players and the PID
baseline in simulation. Our proposed framework significantly reduces the gaps
between different platforms and the Sim2Real gap, enabling the trained agent to
achieve similar performance in both simulation and the real world, driving the
vehicle effectively.

该论文提出了一种强大的 DRL 框架，该框架利用特定于平台的感知模块来提取任务相关信息，并在模拟中训练车道跟踪和超车代理程序，从而促进了 DRL 代理程序对于新的模拟环境和真实世界的无缝转移并极大地缩小了不同平台之间的差距和模拟与现实之间的鸿沟，从而使训练代理程序在模拟和真实世界中能够高效地驾驶车辆。