Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset

Abstract

Macro-management is a crucial aspect of real-time strategy (RTS) games like StarCraft II, which involves high-level decision-making processes such as resource allocation, unit production, and technology development. The MSC dataset, as presented in the original paper, provided an initial platform for researchers to investigate macro-management tasks using deep learning models. However, there are limitations to the dataset and existing baseline models that call for improvement. In this paper, we discuss the challenges and opportunities in enhancing macro-management research in StarCraft II. We propose improvements to the dataset by incorporating new features, addressing data imbalance, and updating preprocessing techniques. Furthermore, we review recent research and state-of-the-art methods in RTS games, such as attention mechanisms, graph neural networks, and reinforcement learning, that could be applied to improve existing tasks or introduce new research directions. We also present our experimental results, highlighting the effectiveness of the proposed improvements and novel approaches. Our goal is to inspire the research community to explore advanced AI techniques and strategies in macro-management and contribute to the development of more capable AI agents in complex RTS games.

Autonomous Driving System Driven by Artificial Intelligence Perception Fusion

“…The disadvantage is that the concatenated feature tensor can be very thick. Taking KITTI data set as an example, the size of the original image is 1392×512, the size of the feature map after four downsamples is 87×32, and the dimension of the feature map of the fourth layer of ResNet-50 is 1024 [18][19][20][21][22].…”

“…The input feature map is represented as Xi ={x1, x2,... , xni}εRb×ni−1×hi×wi, where b is the batch size and hi and wi are the height and width of the feature map. Filter pruning is designed to identify and remove less important weights from WCi Settings, which can be formulated as optimization problems [20][21]: (6) Where F() measures the importance of the weights in the CNN. Delta is a filter, 1 if wi,j is important; If wi,j is not important, it is 0.…”

Clinical Medical Detection and Diagnosis Technology Based on the AlexNet Network Model

“…may bring new breakthroughs in the development of medical diagnostic technology. Real-time monitoring and early warning, using deep learning to monitor a large amount of medical data in real time, may help early detection of signs of disease, and timely warning, thereby improving the cure rate of disease [20]. With the continuous development of technology, there may be more standardization and regulatory development work in the future to regulate the development of this field and protect the rights and interests of patients.…”