A Fully Controllable UAV Using Curriculum Learning and Goal-Conditioned Reinforcement Learning: From Straight Forward to Round Trip Missions

The focus of unmanned aerial vehicle (UAV) path planning includes challenging tasks such as obstacle avoidance and efficient target reaching in complex environments. Building upon these fundamental challenges, an additional need exists for agents that can handle diverse missions like round-trip navigation without requiring retraining for each specific task. In our study, we present a path planning method using reinforcement learning (RL) for a fully controllable UAV agent. We combine goal-conditioned RL and curriculum learning to enable agents to progressively master increasingly complex missions, from single-target reaching to round-trip navigation. Our experimental results demonstrate that the trained agent successfully completed 95% of simple target-reaching tasks and 70% of complex round-trip missions. The agent maintained stable performance even with multiple subgoals, achieving over 75% success rate in three-subgoal missions, indicating strong potential for practical applications in UAV path planning.