Jang, S.-H.; Ahn, W.-J.; Kim, Y.-J.; Hong, H.-G.; Pae, D.-S.; Lim, M.-T. Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles. Electronics2023, 12, 3773.
Jang, S.-H.; Ahn, W.-J.; Kim, Y.-J.; Hong, H.-G.; Pae, D.-S.; Lim, M.-T. Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles. Electronics 2023, 12, 3773.
Jang, S.-H.; Ahn, W.-J.; Kim, Y.-J.; Hong, H.-G.; Pae, D.-S.; Lim, M.-T. Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles. Electronics2023, 12, 3773.
Jang, S.-H.; Ahn, W.-J.; Kim, Y.-J.; Hong, H.-G.; Pae, D.-S.; Lim, M.-T. Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles. Electronics 2023, 12, 3773.
Abstract
Reinforcement Learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like Hindsight Experience Replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines Obstacle Dependent Gaussian (ODG) RL, Soft Actor-Critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.
Keywords
reinforcement learning； meta learning； deep reinforcement learning； autonomous driving； robot operating system
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
