게임 콘텐츠가 점점 복잡해짐에 따라 기존의 수동 테스트 및 스크립트 기반 테스트 방법 은 비용과 테스트 범위 측면에서 한계를 보이고 있다. 본 연구에서는 픽셀 수준의 시각 정 보만을 사용하여 게임의 그래픽 사용자 인터페이스(GUI)와 상호작용하는 딥 강화학습(DRL) 기반 자동 게임 테스트 에이전트를 제안한다. 제안된 에이전트는 ResNet18 기반 시각 인식 모듈과 Proximal Policy Optimization(PPO) 알고리즘을 결합하여, 게임에 대한 어떠한 선 해 정보 없이도 게임 내 장애물을 만났을 때 점프, 웅크리기, 벽 오르기와 같은 회피 방법 을 효과적으로 선택할 수 있다. 실험 결과, 제안된 에이전트는 다양한 장애물 구성 환경에 서 무작위 기준 모델 대비 더 높은 과제 성공률과 안정적인 학습 성능을 보였으며, 이를 통해 블랙박스 게임 환경에서 DRL 기반 자동 테스트의 실현 가능성을 입증하였다.

As the E-commerce market grows, the importance of personalized recommendation systems is increasing. Existing collaborative filtering and content-based filtering methods have shown a certain level of performance, but they have limitations such as cold start, data sparseness, and lack of long-term pattern learning. In this study, we design a matching system that combines a hybrid recommendation system and hyper-personalization technology and propose an efficient recommendation system. The core of the study is to develop a recommendation model that can improve recommendation accuracy and increase user satisfaction compared to existing systems. The proposed elements are as follows. First, the hybrid-hyper-personalization matching system provides recommendation accuracy compared to existing methods. Second, we propose an optimal product matching model that reflects user context using real-time data. Third, we optimize Personalized Recommendation System using deep learning and reinforcement learning. Fourth, we present a method to objectively evaluate recommendation performance through A/B testing.

The threat of North Korea's long-range firepower is recognized as a typical asymmetric threat, and South Korea is prioritizing the development of a Korean-style missile defense system to defend against it. To address this, previous research modeled North Korean long-range artillery attacks as a Markov Decision Process (MDP) and used Approximate Dynamic Programming as an algorithm for missile defense, but due to its limitations, there is an intention to apply deep reinforcement learning techniques that incorporate deep learning. In this paper, we aim to develop a missile defense system algorithm by applying a modified DQN with multi-agent-based deep reinforcement learning techniques. Through this, we have researched to ensure an efficient missile defense system can be implemented considering the style of attacks in recent wars, such as how effectively it can respond to enemy missile attacks, and have proven that the results learned through deep reinforcement learning show superior outcomes.

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.

The video game Tetris is one of most popular game and it is well known that its game rule can be modelled as MDP (Markov Decision Process). This paper presents a DQN (Deep Q-Network) based game agent for Tetris game. To this end, the state is defined as the captured image of the Tetris game board and the reward is designed as a function of cleared lines by the game agent. The action is defined as left, right, rotate, drop, and their finite number of combinations. In addition to this, PER (Prioritized Experience Replay) is employed in order to enhance learning performance. To train the network more than 500000 episodes are used. The game agent employs the trained network to make a decision. The performance of the developed algorithm is validated via not only simulation but also real Tetris robot agent which is made of a camera, two Arduinos, 4 servo motors, and artificial fingers by 3D printing.