Explainable Reinforcement Learning for Trajectory Design in UAV-assisted Wireless Networks

Abstract

Unmanned aerial vehicles (UAVs) used as aerial base stations show significant promise for future wireless communication systems. This paper explores using a UAV as an autonomous agent, navigating over multiple hotspots to serve ground users (GUs) and maximize data transmission rates through strategic trajectory design. Existing interpretability methods often prove insufficient in providing comprehensive insights and generating logical, sequential decisions. In this paper, we propose an explainable reinforcement learning framework designed to produce interpretable and verifiable agent policies. Our method starts with an expert optimizer to solve training examples, enabling the learning agent (UAV) to analyse the solutions. Furthermore, we employ inverse reinforcement learning for data-driven reward function estimation. Additionally, we use a probabilistic Q-table function to understand and explain the actions executed by the expert across diverse environmental contexts, allowing the learning agent to produce interpretable policies that meet reasonable performance goals and easily transferred to unseen environments. Preliminary results indicate that the proposed approach is promising in achieving explainability in RL agents.