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In this chapter on Reinforcement Learning for Edge AI, we explore Warm-Start Reinforcement Learning (RL), which integrates offline training to boost the efficiency of online learning in real-world applications. Warm-Start RL leverages a prior policy, derived from offline training, to give the learning process a significant head start. This method holds promise for edge AI systems, where computational resources are limited and fast adaptation to dynamic environments is essential. However, despite its potential, Warm-Start RL faces challenges in achieving consistent improvements. As empirical studies have shown, while performance can improve rapidly in some cases, it may also stagnate, especially when function approximation is involved.

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Reinforcement Learning for Edge AI

  • 章节

  • 发表日期：

    2025年

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  • Hang Wang,
  • Sen Lin,
  • Junshan Zhang

摘要

In this chapter on Reinforcement Learning for Edge AI, we explore Warm-Start Reinforcement Learning (RL), which integrates offline training to boost the efficiency of online learning in real-world applications. Warm-Start RL leverages a prior policy, derived from offline training, to give the learning process a significant head start. This method holds promise for edge AI systems, where computational resources are limited and fast adaptation to dynamic environments is essential. However, despite its potential, Warm-Start RL faces challenges in achieving consistent improvements. As empirical studies have shown, while performance can improve rapidly in some cases, it may also stagnate, especially when function approximation is involved.