Unmanned aerial vehicle trajectory design in wireless sensor networks: a deep reinforcement learning method

Unmanned Aerial Vehicle (UAV)-powered Wireless Sensor Networks (WSNs) are considered to be a promising solution to the problem of the limited power of the Sensor Nodes (SNs). In this paper, we introduce a UAV-powered WSN system, where multi-UAVs undertake the role of remote charging stations. To optimize the overall power efficiency, we design the collaborative trajectories of the UAVs. In order to solve the problem of trajectory planning, we first model the service process as a Markov decision process (MDP), and then propose a Multi-Agent Deep Reinforcement Learning (MADRL) based algorithm named Modified Multi-agent Deep Deterministic Policy Gradient (M2DDPG), which is learned centrally and executed discretely. The simulation has demonstrated the validity, efficacy, and superior performance of the proposed M2DDPG algorithm compared to the baseline algorithm.