AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning

In this paper we investigate transformer architectures designed for partially observable online reinforcement learning. The self-attention mechanism in the transformer architecture is capable of capturing long-range dependencies and it is the main reason behind its effectiveness in processing sequen...

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Hauptverfasser:	Pramanik, Subhojeet, Elelimy, Esraa, Machado, Marlos C, White, Adam
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning
Online-Zugang:	Volltext bestellen
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