Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable

TL;DR

This paper introduces a multi-agent reinforcement learning framework for wireless sensor networks with mobile chargers, aiming to maximize network lifetime and efficiency through decentralized decision-making and multi-point charging strategies.

Contribution

It presents a novel Decentralized Partially Observable Semi-Markov Decision Process model and an Asynchronous Multi Agent Reinforcement Learning algorithm for efficient network management.

Findings

Enhanced network lifetime through optimized charging strategies

Effective multi-agent cooperation in dynamic environments

Reinforcement learning algorithms adaptable to various network scenarios

Abstract

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).