Version 1
: Received: 29 August 2024 / Approved: 29 August 2024 / Online: 30 August 2024 (03:51:39 CEST)
How to cite:
Shi, H.; Chen, J.; Zhang, F.; Liu, M.; Zhou, M. Achieving Robust Learning Outcomes in Autonomous Driving with DynamicNoise Integration in Deep Reinforcement Learning. Preprints2024, 2024082155. https://doi.org/10.20944/preprints202408.2155.v1
Shi, H.; Chen, J.; Zhang, F.; Liu, M.; Zhou, M. Achieving Robust Learning Outcomes in Autonomous Driving with DynamicNoise Integration in Deep Reinforcement Learning. Preprints 2024, 2024082155. https://doi.org/10.20944/preprints202408.2155.v1
Shi, H.; Chen, J.; Zhang, F.; Liu, M.; Zhou, M. Achieving Robust Learning Outcomes in Autonomous Driving with DynamicNoise Integration in Deep Reinforcement Learning. Preprints2024, 2024082155. https://doi.org/10.20944/preprints202408.2155.v1
APA Style
Shi, H., Chen, J., Zhang, F., Liu, M., & Zhou, M. (2024). Achieving Robust Learning Outcomes in Autonomous Driving with DynamicNoise Integration in Deep Reinforcement Learning. Preprints. https://doi.org/10.20944/preprints202408.2155.v1
Chicago/Turabian Style
Shi, H., Mingyang Liu and Mengjie Zhou. 2024 "Achieving Robust Learning Outcomes in Autonomous Driving with DynamicNoise Integration in Deep Reinforcement Learning" Preprints. https://doi.org/10.20944/preprints202408.2155.v1
Abstract
The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, promising notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to navigate complex driving environments effectively. To tackle this challenge, we propose a novel network called DynamicNoise, designed to significantly boost algorithmic performance by introducing noise into the Deep Q-Network (DQN) and Double Deep Q-Network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, leading to more robust learning outcomes. Our experiments demonstrate a 57.25% improvement in navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the Soft Actor-Critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, substantially surpassing traditional methods. These results confirm that the DynamicNoise network significantly enhances the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.
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.
