I'm thrilled to share that I’ve just released the Contextual Multi-Armed Bandits Library, a comprehensive Python toolkit that brings together a suite of both contextual and non-contextual bandit algorithms. Whether you're delving into reinforcement learning research or building practical applications, this library is designed to accelerate your work.
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I'm thrilled to share that I’ve just released the Contextual Multi-Armed Bandits Library, a comprehensive Python toolkit that brings together a suite of both contextual and non-contextual bandit algorithms. Whether you're delving into reinforcement learning research or building practical applications, this library is designed to accelerate your work.
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What's Inside:
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What's Inside:
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- Contextual Algorithms:
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- Contextual Algorithms:
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- LinUCB
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- LinUCB
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- Epsilon-Greedy
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- Epsilon-Greedy
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- KernelUCB
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- KernelUCB
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- NeuralLinearBandit
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- NeuralLinearBandit
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- DecisionTreeBandit
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- DecisionTreeBandit
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- Non-Contextual Algorithms:
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- Non-Contextual Algorithms:
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- Upper Confidence Bound (UCB)
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- Upper Confidence Bound (UCB)
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- Thompson Sampling
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- Thompson Sampling
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Key Features:
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Key Features:
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- Modular Design: Easily integrate and customize algorithms for your specific needs.
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- Modular Design: Easily integrate and customize algorithms for your specific needs.
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- Comprehensive Documentation: Clear instructions and examples to get you started quickly.
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- Comprehensive Documentation: Clear instructions and examples to get you started quickly.
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- Educational Value: Ideal for learning and teaching concepts in reinforcement learning and decision-making under uncertainty.
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- Educational Value: Ideal for learning and teaching concepts in reinforcement learning and decision-making under uncertainty.
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GitHub Repository: 
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GitHub Repository: 
https://github.com/singhsidhukuldeep/contextual-bandits
https://github.com/singhsidhukuldeep/contextual-bandits
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PyPi: 
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PyPi: 
https://pypi.org/project/contextual-bandits-algos/
https://pypi.org/project/contextual-bandits-algos/
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I am eager to hear your feedback, contributions, and ideas. Feel free to open issues, submit pull requests, or fork the project to make it your own.
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I am eager to hear your feedback, contributions, and ideas. Feel free to open issues, submit pull requests, or fork the project to make it your own.
I'm thrilled to share that I’ve just released the Contextual Multi-Armed Bandits Library, a comprehensive Python toolkit that brings together a suite of both contextual and non-contextual bandit algorithms. Whether you're delving into reinforcement learning research or building practical applications, this library is designed to accelerate your work.

What's Inside:

- Contextual Algorithms:
- LinUCB
- Epsilon-Greedy
- KernelUCB
- NeuralLinearBandit
- DecisionTreeBandit

- Non-Contextual Algorithms:
- Upper Confidence Bound (UCB)
- Thompson Sampling

Key Features:

- Modular Design: Easily integrate and customize algorithms for your specific needs.
- Comprehensive Documentation: Clear instructions and examples to get you started quickly.
- Educational Value: Ideal for learning and teaching concepts in reinforcement learning and decision-making under uncertainty.

GitHub Repository: https://github.com/singhsidhukuldeep/contextual-bandits
PyPi: https://pypi.org/project/contextual-bandits-algos/

I am eager to hear your feedback, contributions, and ideas. Feel free to open issues, submit pull requests, or fork the project to make it your own.
🚀
nwaeneh John6666 AtAndDev louisbrulenaudet nicolay-r
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