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This paper investigates an interference-aware joint path planning and powerallocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) ina sparse suburban environment. The UAV's goal is to fly from an initial pointand reach a destination point by moving along the cells to guarantee therequired quality of service (QoS). In particular, the UAV aims to maximize itsuplink throughput and minimize the level of interference to the ground userequipment (UEs) connected to the neighbor cellular BSs, considering theshortest path and flight resource limitation. Expert knowledge is used toexperience the scenario and define the desired behavior for the sake of theagent (i.e., UAV) training. To solve the problem, an apprenticeship learningmethod is utilized via inverse reinforcement learning (IRL) based on bothQ-learning and deep reinforcement learning (DRL). The performance of thismethod is compared to learning from a demonstration technique called behavioralcloning (BC) using a supervised learning approach. Simulation and numericalresults show that the proposed approach can achieve expert-level performance.We also demonstrate that, unlike the BC technique, the performance of ourproposed approach does not degrade in unseen situations.

Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning