Zusammenfassung
Autonomous driving presents unique challenges, particularly in
transferring agents trained in simulation to real-world
environments due to the discrepancies between the two. To
address this issue, here we propose a robust Deep Reinforcement
Learning (DRL) framework that incorporates platform-dependent
perception modules to extract task-relevant information,
enabling the training of a lane-following and overtaking agent
in simulation. This framework facilitates the efficient transfer
of the DRL agent to new simulated environments and the real
world with minimal adjustments. We assess the performance of the
agent across various driving scenarios in both simulation and
the real world, comparing it to human drivers and a
proportional-integral-derivative (PID) baseline in simulation.
Additionally, we contrast it with other DRL baselines to clarify
the rationale behind choosing this framework. Our proposed
approach helps bridge the gaps between different platforms and
the Simulation to Reality (Sim2Real) gap, allowing the trained
agent to perform consistently in both simulation and real-world
scenarios, effectively driving the vehicle.
Nutzer
