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This chapter explores reinforcement learning (RL) and deep reinforcement learning (DRL) algorithms, both in general and in the context of Age of Information (AoI)-aware UAV-assisted WSN/IoT networks. It discusses the three main classes of RL algorithms: value-based, policy-based, and actor-critic. A range of DRL solutions and algorithms designed to learn optimal policies for some of the Markov Decision Processes (MDPs) introduced in Chap. 2 are examined in this chapter. These DRL approaches are implemented in both single-agent and multi-agent environments.

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Reinforcement Learning and Deep Reinforcement Learning for AoI-Aware UAV-IoT

  • 章节

  • 发表日期：

    2025年

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  • Oluwatosin Ahmed Amodu,
  • Raja Azlina Raja Mahmood,
  • Huda Althumali,
  • Umar Ali Bukar,
  • Nor Fadzilah Abdullah,
  • Chedia Jarray

摘要

This chapter explores reinforcement learning (RL) and deep reinforcement learning (DRL) algorithms, both in general and in the context of Age of Information (AoI)-aware UAV-assisted WSN/IoT networks. It discusses the three main classes of RL algorithms: value-based, policy-based, and actor-critic. A range of DRL solutions and algorithms designed to learn optimal policies for some of the Markov Decision Processes (MDPs) introduced in Chap. 2 are examined in this chapter. These DRL approaches are implemented in both single-agent and multi-agent environments.