Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning
This addresses path planning for unmanned vehicles, but it is incremental as it applies an existing DRL method to a specific domain.
The paper tackled autonomous navigation for unmanned vehicles using Deep Reinforcement Learning, specifically the DDPG algorithm, and found that it outperformed DQN and DDQN in path planning tasks in simulations.
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.