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A Reinforcement Learning-Based Path Optimization for Autonomous Underwater Vehicle Mission Execution in Dynamic Marine Environments
Hyojun Ahn, Shincheon Ahn, Emily Jimin Roh, Ilseok Song, Jooeun Kwon, Sei Kwon, Youngdae Kim, Soohyun Park, Joongheon Kim
http://doi.org/10.5626/JOK.2025.52.6.519
This paper proposes an AOPF (Autonomous Underwater Vehicle Optimal Path Finder) algorithm for AUV mission execution and path optimization in dynamic marine environments. The proposed algorithm utilizes a PPO (Proximal Policy Optimization)-based reinforcement learning method in combination with a 3-degree-of-freedom (DOF) model, enabling a balanced approach between obstacle avoidance and effective target approach. This method is designed to achieve faster convergence and higher mission performance compared to the DDPG (Deep Deterministic Policy Gradient) algorithm. Experimental results demonstrated that the algorithm enabled stable learning and generated efficient paths. Furthermore, the proposed approach shows strong potential for real-world deployment in complex marine environments. It offers scalability to multi-AUV cooperative control scenarios.
A Similarity-Based Multi-Knowledge Transfer Algorithm for Enhancing Learning Efficiency of Reinforcement Learning-Based Autonomous Agent
Yeryeong Cho, Soohyun Park, Joongheon Kim
http://doi.org/10.5626/JOK.2025.52.4.310
This paper proposed a similarity-based multi-knowledge transfer algorithm (SMTRL) to enhance the learning efficiency of autonomous agents in reinforcement learning. SMTRL can calculates the similarity between pre-trained models and the current model and dynamically adjust the knowledge transfer ratio based on this similarity to maximize learning efficiency. In complex environments, autonomous agents face significant challenges when learning independently, as this process can be time-consuming and inefficient, making knowledge transfer essential. However, differences between pre-trained models and actual environments can result in negative transfer, leading to diminished learning performance. To tackle this issue, SMTRL dynamically can adjusts the ratio of knowledge transfer from highly similar pre-trained models, thereby accelerating learning stability. Furthermore, experimental results demonstrated that the proposed algorithm outperformed traditional reinforcement learning and traditional knowledge transfer learning in terms of convergence speed. Therefore, this paper introduces a novel approach to efficient knowledge transfer for autonomous agents and discusses its applicability to complex mobility environments and directions for future research.
Style Transfer Deep Learning Framework for Nighttime Robust Vehicle Detection in On-Road Mobile Platforms
http://doi.org/10.5626/JOK.2019.46.9.968
Car recognition has become an important part of self-driving car technologies. In autonomous driving, vehicle detection techniques are important to prevent vehicle-to-vehicle accidents. Traditional image processing methods for vehicle detection perform car detection via deep learning. Studies indicate that although these methods are effective in more than fifty percent of cases in daytime detection, their performance is insufficient for nighttime recognition. Vehicle detection is one of the tasks involved in minimizing the loss of human lives. Further, the nighttime scenario is more common, and therefore, in this paper, we propose an improved and robust method for detection of the car via filter-based image style transfer. The results of the proposed method were obtained using real-world data and experiments, and indicate the superiority of our method compared with other methods in terms of accuracy of ideal segmentation.
Deep Ensemble Network with Explicit Complementary Model for Accuracy-balanced Classification
http://doi.org/10.5626/JOK.2019.46.9.941
One of the major evaluation metrics for classification systems is average accuracy, while accuracy deviation is another important performance metric used to evaluate various deep neural networks. In this paper, we present a new ensemble-like fast deep neural network, Harmony, that can reduce the accuracy deviation among categories without degrading the overall average accuracy. Harmony consists of three sub-models: the Target model, Complementary model, and Conductor model. In Harmony, an object is classified by using either the Target model or the Complementary model. The Target model is a conventional classification network for general categories, while the Complementary model is a classification network specifically for weak categories that are inaccurately classified by the Target model. The Conductor model is used to select one of the two models. The experimental results indicate that Harmony accurately classifies categories and also, reduces the accuracy deviation among the categories.
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