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CraftGround: A Flexible Reinforcement Learning Environment Based on the Latest Minecraft
Hyeonseo Yang, Minsu Lee, Byoung-Tak Zhang
http://doi.org/10.5626/JOK.2025.52.3.189
This paper presents CraftGround, an innovative reinforcement learning environment based on the latest version of Minecraft (1.21). CraftGround provides flexible experimental setups and supports reinforcement learning in complex 3D environments, offering a variety of observational data, including visual information, audio cues, biome-specific contexts, and in-game statistics. Our experiments evaluated several agents, such as VPT (Video PreTraining), PPO, RecurrentPPO, and DQN, across various tasks, including tree chopping, evading hostile monsters, and fishing. The results indicated that VPT performed exceptionally well due to its pretraining, achieving higher performance and efficiency in structured tasks. In contrast, online learning algorithms like PPO and RecurrentPPO demonstrated a greater ability to adapt to environmental changes, showing improvement over time. These findings highlight CraftGround's potential to advance research on adaptive agent behaviors in dynamic 3D simulations.
Epoch Score: Dataset Verification using Quantitative Data Quality Assessment
Sungryeol Kim, Taewook Hwang, Sangkeun Jung, Yoonhyung Roh
http://doi.org/10.5626/JOK.2023.50.3.250
It is tough to determine whether a dataset is suitable for a model or specified field or whether there is an error. In this paper, we propose an Epoch Score that indicates the degree of difficulty of the data as a score using incorrect answer data obtained through learning several times under the same conditions but different seeds. Through this, we verified KLUE"s Topic Classification dataset, and about 0.8% performance improvement derived by correcting high-scoring data, which we judge to have errors. Epoch Score can be used for all supervised learning regardless of the data type, such as natural language or images, and the performance of the model can be inferred by the area the of the Epoch Score.
Performance Comparison and Analysis Between Neural and Non-neural Autoencoder-based Recommender Systems
http://doi.org/10.5626/JOK.2020.47.11.1078
While deep neural networks have been bringing advances in many domains, recent studies have shown that the performance gain from deep neural networks is not as extensive as reported, compared to the higher computational complexity they require. This phenomenon is caused by the lack of shared experimental settings and strict analysis of proposed methods. In this paper, 1) we build experimental settings for fair comparison between the different recommenders, 2) provide empirical studies on the performance of the autoencoder-based recommender, which is one of the main families in the literature, and 3) analyze the performance of a model according to user and item popularity. With extensive experiments, we found that there was no consistent improvement between the neural and the non-neural models in every dataset and there is no evidence that the non-neural models have been improving over time. Also, the non-neural models achieved better performance on popular item accuracy, while the neural models relatively perform better on less popular items.
Evaluation of Interest Point Detectors for Data Authentication in Wireless Multimedia Sensor Network (WMSN)
http://doi.org/10.5626/JOK.2019.46.2.184
In Wireless Multimedia Sensor Networks (WMSNs), authentication of multimedia data is very important because the data can be used in making crucial decisions. This study evaluates interest point detectors in terms of resilience to channel error occurred in WMSNs, robustness to JPEG compression, and sensitivity to image tampering. SIFT, SURF, ORB, AKAZE, SADDLE and HOG were evaluated with USC-SIPI image database by computing recall and precision between the original images and modified images by channel errors and JPEG compression and tampering. In addition, median filter and Gaussian filter were applied to reduce channel error and quantization errors from JPEG compression respectively and produced significant performance. AKAZE showed best performance for all conditions of experiments. The evaluation of interest point detectors showed the possibility of their application to authentication in WMSNs.
Design and Implementation of a Log-structured Buffer Based on Non-volatile Memory
http://doi.org/10.5626/JOK.2018.45.11.1117
Next-generation non-volatile memory (NVM) technologies, such as PCM and STTMRAM, provide low latency, high bandwidth, non-volatility, and high capacity. Such NVMs are widely used and studied in the field of computer systems and databases for high performance computing. For example, recent researchers have used NVM for journaling buffers and database logging of file systems and have conducted many optimization studies accordingly. As a complement to existing work, this paper focuses on the atomic page update of applications. For example, in a data management application such as a database system, the atomicity of the pages is ensured by performing a redundant write operation with a temporary buffer in order to atomically update multiple pages. However, this redundant write operation can reduce the performance. Therefore, in this paper, we introduce a log-structured buffer manager (LSMB) to improve the performance while ensuring the consistency. LSBM updates the page to NVM by logging and provides buffering. In addition, if there are duplicated pages in the buffer, the old version of the page is removed to reflect only the latest page, which minimizes the I/O and write amount. Experimental results show that LSBM improves the performance of the application and reduces the total write amount.
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