Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks

arXiv:2606.05208v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has long been a powerful solution to various problems in communication networks. However, traditional RL models still face with several limitations. Not only do they rely on large numbers of interactions with the environment, but they are also limited in terms of modeling long-term relationships and tackling partial observability. In recent years, the Transformer model has demonstrated the ability to enhance RL models, allowing them to overcome these issues. Particularly, the self-attention mechanism within the Trans

Source: arXiv cs.LG — read the full report at the original publisher.

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