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Reinforcement Learning With Large Language Models (LLMs) Interaction For Network Services
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  • Hongyang Du ,
  • Ruichen Zhang,
  • Dusit Niyato ,
  • Jiawen Kang ,
  • Zehui Xiong ,
  • Dong In Kim
Hongyang Du

Corresponding Author:[email protected]

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Ruichen Zhang
Dusit Niyato
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Jiawen Kang
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Zehui Xiong
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Dong In Kim
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Artificial Intelligence-Generated Content (AIGC)- related network services, especially image generation-based services, have garnered notable attention due to their ability to cater to diverse user preferences, which significantly impacts the subjective Quality of Experience (QoE). Specifically, different users can perceive the same semantically informed image quite differently, leading to varying levels of satisfaction. To address this challenge and maximize network users’ subjective QoE, we introduce a novel interactive artificial intelligence (IAI) approach using Reinforcement Learning With Large Language Models Interaction (RLLI). RLLI leverages Large Language Model (LLM)-empowered generative agents to simulate user interactions, thereby providing real-time feedback on QoE that encapsulates a range of user personalities. This feedback is instrumental in facilitating the selection of the most suitable AIGC network service provider for each user, ensuring an optimized, personalized experience.
08 Jan 2024Submitted to TechRxiv
10 Jan 2024Published in TechRxiv