Academic Background Deep Reinforcement Learning (DRL) has achieved remarkable success in various fields such as gaming, robotics, navigation, computer vision, and finance. However, existing DRL algorithms generally suffer from low sample efficiency, requiring vast amounts of data and training steps to achieve desired performance. Particularly in co...

Policy Consensus-Based Distributed Deterministic Multi-Agent Reinforcement Learning Research Report Reinforcement Learning (RL) has made significant breakthroughs in recent years in various fields such as robotics, smart grids, and autonomous driving. However, in real-world scenarios, multi-agent collaboration problems, also known as Multi-Agent Re...

Satellite-Assisted 6G Wide-Area Edge Intelligence: Dynamics-Aware Task Offloading and Resource Allocation for Remote IoT Services Background Introduction With the advent of the 6G mobile communication network, the traditional Internet of Things (IoT) architecture is gradually transforming into the new paradigm of the intelligent Internet of Everyth...

Precision Autofocus in Optical Microscopy with Liquid Lenses Controlled by Deep Reinforcement Learning Academic Background Microscopic imaging plays a crucial role in scientific research, biomedical studies, and engineering applications. However, traditional microscopes and autofocus techniques face hardware limitations and slow software speeds in ...

Autonomous Navigation and Walking Wheel-Leg Robot Background Introduction The acceleration of urbanization has posed significant challenges for supply chain logistics, especially for last-mile delivery. As traffic pressure increases and the demand for faster delivery services rises, particularly with complex routes indoors and on city streets, trad...