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
The increasing complexity and scale of communication networks, coupled with the computational advancements allowing for more sophisticated AI models like Transformers, drive the need for enhanced control mechanisms.
Improved AI control in communication networks allows for more efficient, resilient, and adaptive infrastructure, crucial for supporting future data demands and real-time applications.
The ability to manage communication network dynamics with AI models capable of long-term planning and handling partial observability reduces reliance on traditional, less adaptive control systems.
- · Telecommunication companies
- · AI software providers
- · Data center operators
- · Legacy network management solution providers
- · Infrastructure reliant on static configurations
Communication networks become more autonomous and self-optimizing, reducing operational overhead and improving service quality.
Enhanced network performance and reliability enable the proliferation of new real-time applications and services that require ultra-low latency and high availability.
The integration of advanced AI could create new vulnerabilities if not properly secured, leading to novel cyber-physical threats within critical infrastructure.
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