Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning

Analysis of invasive sports such as soccer is challenging because the game situation changes continuously in time and space, and multiple agents individually recognize the game situation and make decisions. Previous studies using deep reinforcement learning have often considered teams as a single agent and valued the teams and players who hold the ball in each discrete event. Then it was challenging to value the actions of multiple players, including players far from the ball, in a spatiotemporally continuous state space. In this paper, we propose a method of valuing possible actions for on- and off-ball soccer players in a single holistic framework based on multi-agent deep reinforcement learning. We consider a discrete action space in a continuous state space that mimics that of Google research football and leverages supervised learning for actions in reinforcement learning. In the experiment, we analyzed the relationships with conventional indicators, season goals, and game ratings by experts, and showed the effectiveness of the proposed method. Our approach can assess how multiple players move continuously throughout the game, which is difficult to be discretized or labeled but vital for teamwork, scouting, and fan engagement.Comment: 12 pages, 4 figure

Runner re-identification from single-view video in the open-world setting

In many sports, player re-identification is crucial for automatic video processing and analysis. However, most of the current studies on player re-identification in multi- or single-view sports videos focus on re-identification in the closed-world setting using labeled image dataset, and player re-identification in the open-world setting for automatic video analysis is not well developed. In this paper, we propose a runner re-identification system that directly processes single-view video to address the open-world setting. In the open-world setting, we cannot use labeled dataset and have to process video directly. The proposed system automatically processes raw video as input to identify runners, and it can identify runners even when they are framed out multiple times. For the automatic processing, we first detect the runners in the video using the pre-trained YOLOv8 and the fine-tuned EfficientNet. We then track the runners using ByteTrack and detect their shoes with the fine-tuned YOLOv8. Finally, we extract the image features of the runners using an unsupervised method using the gated recurrent unit autoencoder model. To improve the accuracy of runner re-identification, we use dynamic features of running sequence images. We evaluated the system on a running practice video dataset and showed that the proposed method identified runners with higher accuracy than one of the state-of-the-art models in unsupervised re-identification. We also showed that our unsupervised running dynamic feature extractor was effective for runner re-identification. Our runner re-identification system can be useful for the automatic analysis of running videos.Comment: 18 pages, 8 figure

Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations

Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generate flexible and diverse behaviors in cyberspace; however, when modeling real-world biological multi-agents, there is a domain gap between behaviors in the source (i.e., real-world data) and the target (i.e., cyberspace for RL), and the source environment parameters are usually unknown. In this paper, we propose a method for adaptive action supervision in RL from real-world demonstrations in multi-agent scenarios. We adopt an approach that combines RL and supervised learning by selecting actions of demonstrations in RL based on the minimum distance of dynamic time warping for utilizing the information of the unknown source dynamics. This approach can be easily applied to many existing neural network architectures and provide us with an RL model balanced between reproducibility as imitation and generalization ability to obtain rewards in cyberspace. In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines. In particular, we used the tracking data of professional football players as expert demonstrations in football and show successful performances despite the larger gap between behaviors in the source and target environments than the chase-and-escape task.Comment: 14 pages, 5 figure

Peplomycin-induced DNA repair synthesis in permeable mouse ascites sarcoma cells.

DNA repair synthesis induced in permeable mouse ascites sarcoma cells by peplomycin, an antitumor antibiotic, was studied. Mouse ascites sarcoma (SR-C3H/He) cells were permeabilized with a low concentration of Triton X-100 in an isotonic condition. Permeable cells were treated with an appropriate concentration of peplomycin to introduce single-strand breaks in permeable cell DNA. DNA repair synthesis in peplomycin-treated permeable cells was measured by incubating the cells with four deoxynucleoside triphosphates in an appropriate buffer system. The DNA repair synthesis was enhanced by ATP and NaCl at near physiological concentrations. More than 90% of DNA synthesis in the present system depended on the peplomycin-treatment. The repair nature of the DNA synthesis was confirmed by a BrdUMP density shift technique. The repair patches were largely completed and ligated in the presence of ATP. Analyses using selective inhibitors for DNA polymerases showed that both DNA polymerase Beta and aphidicolin-sensitive DNA polymerases (DNA polymerase alpha and/or delta) were involved in the repair DNA synthesis.&#60;/P&#62;</p

The Institutional Presidency from a Comparative Perspective: Argentina and Brazil since the 1980s

This paper focuses on the evolution of the institutional presidency - meaning the cluster of agencies that directly support the chief of the executive - in Argentina and Brazil since their redemocratization in the 1980s. It investigates what explains the changes that have come about regarding the size of the institutional presidency and the types of agency that form it. Following the specialized literature, we argue that the growth of the institutional presidency is connected to developments occurring in the larger political system - that is, to the political challenges that the various presidents of the two countries have faced. Presidents adjust the format and mandate of the different agencies under their authority so as to better manage their relations with the political environment. In particular, we argue that the type of government (coalition or single-party) has had consequences for the structure of the presidency or, in other words, that different cabinet structures pose different challenges to presidents. This factor has not played a significant role in presidency-related studies until now, which have hitherto mostly been based on the case of the United States. Our empirical references, the presidencies of Argentina and Brazil, typical cases of coalitional as well as single-party presidentialism respectively allow us to show the impact of the type of government on the number and type of presidential agencies