Deep Reinforcement Learning (DRL) shows good performance for optimizing battery energy storage systems (BESS) coordinated operations with photovoltaic plants (PV), yet most studies rely on simulations. Bridging the gap to practical application requir...
Traditional asset allocation rules, while effective in stable phases, tend to erode once markets enter volatile regimes or undergo structural breaks. Research in deep reinforcement learning (DRL) has usually emphasized raw-return rewards, leaving asi...
AI computing's rapid expansion is steeply increasing data center electricity use, intensifying sustainability concerns. We develop the first framework that couples deep reinforcement learning (DRL) control with cost-effective optimization to boost ef...
Humans have to respond to everyday tasks with goal-directed actions in complex and time-continuous environments. However, modeling human behavior in such environments has been challenging. Deep Q-networks (DQNs), an application of deep learning used ...
Haplotype assembly is the problem of reconstructing the combination of alleles on the maternally and paternally inherited chromosome copies. Individual haplotypes are essential to our understanding of how combinations of different variants impact phe...
A denial of service (DoS) attack is an essential and nonstop threat to cybersecurity. Generally, DoS attacks are executed by forcing a victim's computer to reset and consume its sources. Distributed DoS (DDoS) is the most underlined and significant a...
To develop and evaluate a deep reinforcement learning (RL) framework for rapid and automatic machine parameter optimization of volumetric modulated arc therapy (VMAT) treatment plans for localized prostate cancer.A multi-task policy network combining...
Deep reinforcement learning (DRL) has been applied to real-time control (RTC) of urban drainage systems (UDSs), with impressive performance and efficiency in reducing urban flooding and combined sewer overflows (CSO). However, for complex UDSs, learn...
The erratic nature of cardiac rhythms can precipitate a multitude of pathologies. Consequently, the endeavor to achieve stabilization of the human heartbeat has garnered significant scholarly interest in recent years. In this context, an adaptive non...
Due to concentrated conflicts, on-ramp merging is an important scenario in the study of new hybrid traffic control. Current research mainly focuses on optimizing the vehicle passage sequence of ramp vehicles merging with mainline vehicles in single-l...
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