The popular data-driven control algorithm such as iterative learning control (ILC) and
reinforcement learning control (RLC) is inefficient in the continuous chemical processes for …

The developments in reinforcement learning provide a powerful and efficient learning
framework for autonomous robotic systems. However, prior works rarely embed historical …

During the flyby mission of small celestial bodies in deep space, it is hard for spacecraft to
take photos at proper positions only rely on ground-based scheduling, due to the long …

Unmanned aerial vehicles (UAVs) path planning refers to exploring the optimal flight
trajectory from the starting point to the destination that satisfies the UAV under specific …

The paper focuses on development of model-free and distributed Reinforcement Learning
(RL) algorithms for multi-agent networks. The goal is to learn optimal control policies directly …

At present, autonomous driving decision-making solutions take few elements into account
while ignoring the unpredictable nature of driving behavior, which makes it challenging to …

In a debris mitigation mission, a crucial phase of the proximity operation for a space
manipulator is chasing a capture point on a noncooperative target satellite. Knowing the …

This study focuses on enhancing the autonomous path planning capabilities of intelligent
mobile robots, which are complex mechatronic systems combining various functionalities …

[目的] 为解决非结构化环境下采用深度强化学习进行采摘机械臂路径规划时存在的效率低,
采摘路径规划成功率不佳的问题, 提出了一种非结构化环境下基于深度强化学习(Deep …

摘摇要: 深度强化学习(deep reinforcement learning, DRL) 将深度学习从高维数据提取低维
特征的能力与强化学习的决策能力相结合, 是移动通信资源管理与优化的高效算法之一 …