dimgold/Artificial_Curiosity
Adaptive Reinforcement Learning of curious AI basketball agents
This project helps basketball coaches, analysts, or enthusiasts understand and predict NBA shot success rates. By inputting historical NBA player shot data, it outputs insights into which shots are likely to succeed and simulates game scenarios where an AI agent learns to optimize its shot selection. It's designed for someone interested in advanced basketball analytics and AI-driven strategy.
No commits in the last 6 months.
Use this if you want to explore how AI can learn optimal basketball shot strategies and predict success based on extensive historical NBA data.
Not ideal if you're looking for a simple tool to track player statistics or for real-time game analysis during a live match.
Stars
22
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7
Language
Jupyter Notebook
License
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Category
Last pushed
Oct 28, 2017
Commits (30d)
0
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