A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games. Because these games have complicated rules, an action sampled from the full discrete action distribution predicted by the learned policy is likely to be invali...

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Principais autores:	Shengyi Huang, Santiago Ontañón
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	LibraryPress@UF 2022-05-01
סדרה:	Proceedings of the International Florida Artificial Intelligence Research Society Conference
נושאים:	reinforcement learning deep learning deep reinforcement learning real-time strategy games implementation details invalid action masking
גישה מקוונת:	https://journals.flvc.org/FLAIRS/article/view/130584
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