33 citations · 125 across the 19 of their papers we have counts for
7 papers · 1 filter
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
Hiroshi Nakahara, Kazushi Tsutsui, Kazuya Takeda +1
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 sit…
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Keisuke Fujii, Kazushi Tsutsui, Atom Scott +3
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 generat…
Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis
Calvin C. K. Yeung, Tony Sit, Keisuke Fujii
With recently available football match event data that record the details of football matches, analysts and researchers have a great opportunity to develop new performance metrics,…
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda +1
Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game…
Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball
Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii
In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure…
Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi +3
Evaluation of intervention in a multiagent system, e.g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is c…
How does AI play football? An analysis of RL and real-world football strategies
Atom Scott, Keisuke Fujii, Masaki Onishi
Recent advances in reinforcement learning (RL) have made it possible to develop sophisticated agents that excel in a wide range of applications. Simulations using such agents can p…