Shared Backbone PPO for Multi-UAV Communication Coverage with Connection Preservation

TL;DR

This paper introduces a Shared Backbone PPO algorithm for multi-UAV communication coverage that enhances training efficiency and performance by sharing network components and incorporating graph information aggregation.

Contribution

The paper presents a novel Shared Backbone PPO method with a graph aggregation module, improving multi-UAV swarm cooperation and connectivity preservation.

Findings

The proposed method outperforms standard PPO in multi-UAV tasks.

Sharing the base module improves training efficiency and performance.

Graph information aggregation enhances communication and cooperation among agents.

Abstract

This paper proposes a Shared Backbone Proximal Policy Optimization (Shared Backbone PPO) algorithm. By sharing the base module between the Actor and Critic networks, the algorithm achieves efficient training and improved performance. The algorithm is implemented in a connectivity-preserving multi-UAV swarm communication coverage task and compared with the standard PPO algorithm. Experimental results demonstrate that the proposed method achieves superior performance. Furthermore, a graph information aggregation module is incorporated into the model architecture to accommodate the communication conditions among agents. With the integration of this module, the algorithm remains effective, and the trained agent swarm exhibits a higher level of cooperation.