DRL-driven padel players: Simulating padel matches through deep reinforcement learning in real and hypothetical scenarios

Recent advances in Deep Reinforcement Learning (DRL) have opened new avenues for sport research. DRL allows virtual agents to learn and solve complex tasks with minimal input, which means that models can be trained with little or no data collection. This enables the creation of sport simulations tha...

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Bibliographic Details
Published in:Journal of sports sciences 2025-09, Vol.43 (17), p.1742-1761
Main Authors: Javadiha, Mohammadreza, Ji, Jia Long, Zhou, Wenqi, Lacasa, Enrique, Andújar, Carlos
Format: Article
Language:English
Subjects:
artificial intelligence
Athletic Performance - psychology
Computer Simulation
Data collection
Deep Learning
Humans
imitation learning
Mathematical models
Racket sports
Reinforcement
Reinforcement Machine Learning
Reinforcement, Psychology
Robotics
virtual agents
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