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- Title
Path-Tracking Control Strategy of Unmanned Vehicle Based on DDPG Algorithm.
- Authors
Yao, Jialing; Ge, Zhen
- Abstract
This paper proposes a deep reinforcement learning (DRL)-based algorithm in the path-tracking controller of an unmanned vehicle to autonomously learn the path-tracking capability of the vehicle by interacting with the CARLA environment. To solve the problem of the high estimation of the Q-value of the DDPG algorithm and slow training speed, the controller adopts the deep deterministic policy gradient algorithm of the double critic network (DCN-DDPG), obtains the trained model through offline learning, and sends control commands to the unmanned vehicle to make the vehicle drive according to the determined route. This method aimed to address the problem of unmanned-vehicle path tracking. This paper proposes a Markov decision process model, including the design of state, action-and-reward value functions, and trained the control strategy in the CARLA simulator Town04 urban scene. The tracking task was completed under various working conditions, and its tracking effect was compared with the original DDPG algorithm, model predictive control (MPC), and pure pursuit. It was verified that the designed control strategy has good environmental adaptability, speed adaptability, and tracking performance.
- Subjects
AUTONOMOUS vehicles; REINFORCEMENT learning; REMOTELY piloted vehicles; ALGORITHMS; MARKOV processes
- Publication
Sensors (14248220), 2022, Vol 22, Issue 20, pN.PAG
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22207881