A platform-agnostic deep reinforcement learning framework for effective Sim2Real transfer towards autonomous driving.

ABSTRACT: 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. Additional

SUBMITTER: Li D 

PROVIDER: S-EPMC11487131 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature