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The connectivity-aware path design is crucial in the effective deployment ofautonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning(RL) algorithms have become the popular approach to solving this type ofcomplex problem, but RL algorithms suffer slow convergence. In this paper, wepropose a Transfer Learning (TL) approach, where we use a teacher policypreviously trained in an old domain to boost the path learning of the agent inthe new domain. As the exploration processes and the training continue, theagent refines the path design in the new domain based on the subsequentinteractions with the environment. We evaluate our approach considering an olddomain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacherpath policy, previously trained at sub-6 GHz path, is the solution to aconnectivity-aware path problem that we formulate as a constrained MarkovDecision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network(DQN) to solve the path design at sub-6 GHz that guarantees connectivityconstraint satisfaction. We empirically demonstrate the effectiveness of ourapproach for different urban environment scenarios. The results demonstratethat our proposed approach is capable of reducing the training timeconsiderably at mmWave.

A Transfer Learning Approach for UAV Path Design with Connectivity Outage Constraint