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.

翻译：深度强化学习已在多个研究领域展现出解决复杂任务的卓越能力。然而，由于仿真环境与真实世界之间存在显著差异，将深度强化学习智能体迁移至现实场景仍面临挑战。为应对这一问题，本文提出一种鲁棒的深度强化学习框架，该框架利用平台相关的感知模块提取任务相关信息，并在仿真环境中训练车道保持与超车智能体。该框架支持将深度强化学习智能体以最小代价无缝迁移至新的仿真环境及真实世界。我们通过仿真与真实场景中的多种驾驶情境评估智能体性能，并在仿真环境中与人类驾驶员及PID基线进行对比。实验表明，所提框架显著缩小了不同平台间的差异以及Sim2Real差距，使经过训练的智能体在仿真与真实世界中均能实现相近的性能表现，有效完成车辆驾驶任务。