UAV Head-On Situation Maneuver Generation Using Transfer-Learning-Based Deep Reinforcement Learning

Abstract

AbstractRecently, the demand for unmanned aerial vehicle technology has increased. In particular, AI pilots through reinforcement learning (RL) are more flexible than those using rule-based methods. Further, AI pilots with RL are expected to replace human pilots in the future. In a recent study, rather than completely replacing humans, studies on AI pilots are conducted toward the collaboration between man and unmanned aircraft. AI pilots have several advantages over humans. For example, on the one hand, human pilots avoid head-on situations due to collision. On the other hand, AI pilots may prefer head-on situations to finish the episode quickly. This study proposes a two-circle-based transfer learning method to demonstrate excellent performance in head-on situations. Based on the experimental results, the proposed two-circle-based multi-task transfer learning model outperforms the model without transfer learning-based RL. A study on transfer-learning-based learning technique has been conducted. However, it had a one-circle-based learning technique was specialized only for tail-chasing, limiting its application (Bae et al. in IEEE Access 11:26427–26440, 2023). Practically, the proposed two-circle-based learning technique outperforms the one-circle-based transfer learning technique in head-on situations.