Smart balancing of E-scooter sharing systems via deep reinforcement learning: a preliminary study
G Losapio, F Minutoli, V Mascardi… - Intelligenza …, 2022 - content.iospress.com
Nowadays, micro-mobility sharing systems have become extremely popular. Such systems
consist in fleets of dockless electric vehicles which are deployed in cities, and used by …
consist in fleets of dockless electric vehicles which are deployed in cities, and used by …
Safe Exploration Reinforcement Learning for Load Restoration using Invalid Action Masking
This paper addresses the load restoration problem after a power outage event. Our primary
proposed methodology uses a multi-agent reinforcement learning method to make the …
proposed methodology uses a multi-agent reinforcement learning method to make the …
Sim-to-Real Transfer of Adaptive Control Parameters for AUV Stabilization under Current Disturbance
Learning-based adaptive control methods hold the premise of enabling autonomous agents
to reduce the effect of process variations with minimal human intervention. However, its …
to reduce the effect of process variations with minimal human intervention. However, its …
Hierarchical Decentralized Optimal Control and Reconfiguration of Networked Microgrids in the Power Distribution System
A Ingalalli - 2023 - search.proquest.com
Advancements in information and communication technology, decentralized digital
economic structures, and data-driven learning-based technology have transformed …
economic structures, and data-driven learning-based technology have transformed …
Toolpath Generation and Optimization for Metal Additive Manufacturing
M Qin - 2022 - search.proquest.com
Metal additive manufacturing (AM) has been regarded as a promising technology, especially
for fabricating structures with complicated or customized designed features. Toolpath plays a …
for fabricating structures with complicated or customized designed features. Toolpath plays a …