DESEM: Depthwise Separable Convolution-Based Multimodal Deep Learning for In-Game Action Anticipation

DESEM: Depthwise Separable Convolution-Based Multimodal Deep Learning for In-Game Action Anticipation

In real-time strategy (RTS) games, to defeat their opponents, players need to choose and implement the correct sequential actions. Because RTS games like StarCraft II are real-time, players have a very limited time to choose how to develop their strategy. In addition, players can only partially obse...

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Bibliographic Details
Main Authors: Changhyun Kim, Jinsoo Bae, Insung Baek, Jaeyoon Jeong, Young Jae Lee, Kiwoong Park, Sang Heun Shim, Seoung Bum Kim
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Action anticipation
depthwise separable convolution
game artificial intelligence
multimodal deep learning
weighted loss function
Online Access:https://ieeexplore.ieee.org/document/10109657/

Internet

https://ieeexplore.ieee.org/document/10109657/

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