• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.281-288
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• 2022

AGVs are often used in industrial applications to transport heavy materials around a large industrial building, such as factories or warehouses. In particular, in fulfillment centers their usefulness is maximized for automation. To increase productivity in warehouses such as fulfillment centers, sophisticated path planning of AGVs is required. We propose a scheme that can be applied to QMIX, a popular cooperative MARL algorithm. The performance was measured with three metrics in several fulfillment center layouts, and the results are presented through comparison with the performance of the existing QMIX. Additionally, we visualize the transport paths of trained AGVs for a visible analysis of the behavior patterns of the AGVs as heat maps.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.79-88
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• 2024

With the advancement of unmanned aerial vehicle technology, swarm drones are increasingly being deployed across various domains, including disaster response and military operations, where their effectiveness is particularly pronounced. These swarm drones leverage real-time data sharing and decision-making capabilities to execute tactical missions, making collaborative behavior essential in complex battlefield environments. However, traditional rule-based behavior mechanisms face limitations as environmental complexity escalates. This paper explores the potential of applying multi-agent reinforcement learning (MARL) to swarm drone models and proposes strategies to enhance their mission success rates. By utilizing QPLEX and Prioritized Experience Replay (PER), we present methods aimed at improving learning efficiency. Validation through the SMACv2 simulator reveals that the proposed approach achieves faster learning convergence and higher mission success rates compared to existing MARL algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.175-181
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• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• Journal of Korean Association for Spatial Structures
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• v.23 no.2
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• pp.29-36
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• 2023

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.