The purpose of this article is aimed to solve the problem associated with autonomous driving
on the natural paths of planets. The contribution of this work is to propose an improved deep …

提出了一种基于多类型传感数据训练自动驾驶策略的方法, 采用不同卷积网络对高维图像数据和
低维目标级传感数据进行特征提取, 然后对提取特征进行组合, 采用组合特征学**自动驾驶策略 …

As the artificial intelligence technology advances, intellectualization has become an
important development trend for the future aero-engine industry. The intelligent aero-engine …

This article revolves around autonomous driving, mainly introducing the autonomous driving
cloud platform based on the reinforcement learning to improve the autonomous driving of …

针对多随机场景下用户可选择需求响应(CCR) 的场景组合激增问题, 利用深度**化学**算法实现
CCR 群组的优选及其所包含节点的优化调度. 首先, 根据CCR 优化调度的约束条件与目标函数 …

Q-learning is a typical model-free algorithm in reinforcement learning to achieve a goal by
interacting with an uncertain environment. However, conventional Q-learning cannot reach …

Self-driving is a significant application of deep reinforcement learning. We present a deep
reinforcement learning algorithm for control policies of self-driving vehicles. This method …

针对在交叉口自动驾驶车辆与其他车辆直行冲突的问题, 构建自动驾驶汽车行为决策模型,
采用深度**化学**对自动驾驶汽车通过道路交叉口进行训练, 让自动驾驶汽车自主决策学** …

Reliability, efficiency and generalization are basic evaluation criteria for a vehicle automated
driving system. This paper proposes an automated driving decision-making method based …

优良的可靠性, 学**效率和模型泛化能力是车辆自动驾驶系统研究的基本要求.
基于深度**化学**理论框架提出了一种用于车辆自动驾驶决策的WGAIL-DDPG (λ) …