Abstract: State-of-charge (SoC) balancing in distributed energy storage systems (DESS) is crucial but challenging. Traditional deep reinforcement learning approaches struggle with real-world ...

Recently, there have been significant research interests in training large language models (LLMs) with reinforcement learning (RL) on real-world tasks, such as multi-turn code generation. While online ...

To provide quantitative analysis of strategic confrontation game such as cross-border trades like tariff disputes and competitive scenarios like auction bidding, we propose an alternating Markov ...

Every decision a customer makes—whether to browse, purchase, or churn—can often be modeled as a sequence of states. By understanding these transitions, businesses can predict behaviors, optimize ...

2. Partially Observable Markov Decision Processes (POMDPs): Extending MDPs, these models account for scenarios where agents operate with incomplete or unreliable information. Applications include ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Markov state models (MSM) are a popular statistical method for analyzing the ...

Abstract: Incorporating edge and cloud computing with robotics provides extended options for robots to perform real-time sensing and actuation operations in various cyber–physical systems (CPSs), ...

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional ...

Microsoft has introduced Python integration in Excel, allowing users to perform advanced data analysis seamlessly. The new functionality eliminates the need for additional software installation, ...

In the era of Industry 4.0, order scheduling is a crucial link in the production of manufacturing enterprises. In view of order scheduling in manufacturing enterprises, a finite horizon Markov ...