Shaoliang Zhang 张绍良 The complexity in basketball performance: quantifying the independent effects of game load, technical–tactical approaches, and contextual factors on player performance
Reinforcement Learning Papers - GitHub Related papers for Reinforcement Learning (we mainly focus on single-agent) Since there are tens of thousands of new papers on reinforcement learning at each conference every year, we are only able to list those we read and consider as insightful
From statistical modeling to machine learning: advancing basketball . . . Traditional statistical methods provided interpretable insights into player box-score metrics and team tactics, but the rapid growth of high-resolution tracking data and advances in computation have opened new opportunities
Deep Reinforcement Learning: A Chronological Overview and Methods - MDPI Introduction: Deep reinforcement learning (deep RL) integrates the principles of reinforcement learning with deep neural networks, enabling agents to excel in diverse tasks ranging from playing board games such as Go and Chess to controlling robotic systems and autonomous vehicles
Towards Playing Full MOBA Games with Deep Reinforcement Learning The paper provides an AI learning method to develop AI agents conducting game balance testing and strategy exploration, which are helpful to improve player experience and improving players’ strategies, on MOBA games
Potential Play Evaluation with Learning-Based Agent Modeling Next, the inverse approach for player and team evaluation from data and the forward approach for virtual simulations are introduced, and the advantages of using learning-based methods over traditional simulation techniques are highlighted