The thesis proposes a generalized charging framework for multiple mobile chargers to maximize the network lifetime and ensure target coverage and connectivity in large scale WRSNs. Moreover, a multi-point charging model is leveraged to enhance charging efficiency, where the MC can charge multiple sensors simultaneously at each charging location. The thesis proposes an effective Decentralized Partially Observable Semi-Markov Decision Process (Dec POSMDP) model that promotes Mobile Chargers (MCs) cooperation and detects optimal charging locations based on realtime network information. Furthermore, the proposal allows reinforcement algorithms to be applied to different networks without requiring extensive retraining. To solve the Dec POSMDP model, the thesis proposes an Asynchronous Multi Agent Reinforcement Learning algorithm (AMAPPO) based on the Proximal Policy Optimization algorithm (PPO).

翻译：本文提出了一种面向大规模无线可充电传感器网络的通用多移动充电器调度框架，旨在最大化网络寿命，同时确保目标区域的覆盖与网络连通性。该框架采用多点充电模型以提升充电效率，允许移动充电器在每个停驻点为多个传感器同时充电。本文提出了一种有效的去中心化部分可观测半马尔可夫决策过程模型，该模型能促进移动充电器之间的协作，并基于实时网络信息确定最优充电位置。此外，该方案使强化学习算法能够适用于不同网络场景，而无需进行大量重新训练。为求解该决策模型，本文基于近端策略优化算法，提出了一种异步多智能体近端策略优化算法。