Engineering and TechnologyCommunications Engineering
A platform-agnostic deep reinforcement learning framework for effective Sim2Real transfer towards autonomous driving
D. Li and O. Okhrin
Discover a groundbreaking Deep Reinforcement Learning framework for autonomous driving developed by Dianzhao Li and Ostap Okhrin that conquers the Sim2Real transfer challenge. This innovative approach enhances lane-following and overtaking capabilities using simulated environments, ensuring seamless performance in the real world.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Transportation
Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments
Y. Fei, L. Xing, et al.
Engineering and Technology
A rapid and effective method for alloy materials design via sample data transfer machine learning
L. Jiang, Z. Zhang, et al.
Physics
Realizing a deep reinforcement learning agent for real-time quantum feedback
K. Reuer, J. Landgraf, et al.
Engineering and Technology
SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning
Y. Song, M. Zhang, et al.
