Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913448398422016 |
|---|---|
| author | Xu, Letian Liu, Jiabei Zhao, Haopeng Zheng, Tianyao Jiang, Tongzhou Liu, Lipeng |
| author_facet | Xu, Letian Liu, Jiabei Zhao, Haopeng Zheng, Tianyao Jiang, Tongzhou Liu, Lipeng |
| contents | This paper explores the method of achieving autonomous navigation of unmanned vehicles through Deep Reinforcement Learning (DRL). The focus is on using the Deep Deterministic Policy Gradient (DDPG) algorithm to address issues in high-dimensional continuous action spaces. The paper details the model of a Ackermann robot and the structure and application of the DDPG algorithm. Experiments were conducted in a simulation environment to verify the feasibility of the improved algorithm. The results demonstrate that the DDPG algorithm outperforms traditional Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms in path planning tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18962 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning Xu, Letian Liu, Jiabei Zhao, Haopeng Zheng, Tianyao Jiang, Tongzhou Liu, Lipeng Robotics Machine Learning This paper explores the method of achieving autonomous navigation of unmanned vehicles through Deep Reinforcement Learning (DRL). The focus is on using the Deep Deterministic Policy Gradient (DDPG) algorithm to address issues in high-dimensional continuous action spaces. The paper details the model of a Ackermann robot and the structure and application of the DDPG algorithm. Experiments were conducted in a simulation environment to verify the feasibility of the improved algorithm. The results demonstrate that the DDPG algorithm outperforms traditional Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms in path planning tasks. |
| title | Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2407.18962 |