Reinforcement Learning-Based Decode-and-Forward UAV Relay Trajectory Optimization
Unmanned Aerial Vehicles (UAVs) are promising relay platforms due to their flexible deployment and high probability of line-of-sight (LoS) connectivity. This paper compares three deep reinforcement learning (DRL) algorithms-Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Recurrent PPO with LSTM memory-for joint UAV trajectory and energy optimization in UAV based relay systems. The problem formulated is a non-convex optimization problem that minimizes UAV propulsion energy while satisfying Quality of Service (QoS) and mobility constraints under realistic 3GPP channel conditions. Simulation results show that all methods achieve over 99% QoS satisfaction. SAC exhibits the fastest convergence, whereas the proposed Recurrent PPO achieves the lowest energy consumption (44.72 kJ), reducing energy usage by 5.1% compared with PPO. These results highlight the trade-off between convergence speed and energy efficiency in DRL-based UAV relay optimization.