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Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
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.
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
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.
Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball
The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution.
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