content.raw: 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.
content.type: text
content.value: 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.
content.type: new_line
content.type: new_line
content.raw: What's Inside:
content.type: text
content.value: What's Inside:
content.type: new_line
content.type: new_line
content.raw: - Contextual Algorithms:
content.type: text
content.value: - Contextual Algorithms:
content.type: new_line
content.raw: - LinUCB
content.type: text
content.value: - LinUCB
content.type: new_line
content.raw: - Epsilon-Greedy
content.type: text
content.value: - Epsilon-Greedy
content.type: new_line
content.raw: - KernelUCB
content.type: text
content.value: - KernelUCB
content.type: new_line
content.raw: - NeuralLinearBandit
content.type: text
content.value: - NeuralLinearBandit
content.type: new_line
content.raw: - DecisionTreeBandit
content.type: text
content.value: - DecisionTreeBandit
content.type: new_line
content.type: new_line
content.raw: - Non-Contextual Algorithms:
content.type: text
content.value: - Non-Contextual Algorithms:
content.type: new_line
content.raw: - Upper Confidence Bound (UCB)
content.type: text
content.value: - Upper Confidence Bound (UCB)
content.type: new_line
content.raw: - Thompson Sampling
content.type: text
content.value: - Thompson Sampling
content.type: new_line
content.type: new_line
content.raw: Key Features:
content.type: text
content.value: Key Features:
content.type: new_line
content.type: new_line
content.raw: - Modular Design: Easily integrate and customize algorithms for your specific needs.
content.type: text
content.value: - Modular Design: Easily integrate and customize algorithms for your specific needs.
content.type: new_line
content.raw: - Comprehensive Documentation: Clear instructions and examples to get you started quickly.
content.type: text
content.value: - Comprehensive Documentation: Clear instructions and examples to get you started quickly.
content.type: new_line
content.raw: - Educational Value: Ideal for learning and teaching concepts in reinforcement learning and decision-making under uncertainty.
content.type: text
content.value: - Educational Value: Ideal for learning and teaching concepts in reinforcement learning and decision-making under uncertainty.
content.type: new_line
content.type: new_line
content.raw: GitHub Repository: 
content.type: text
content.value: GitHub Repository: 
content.href: https://github.com/singhsidhukuldeep/contextual-bandits
content.raw: https://github.com/singhsidhukuldeep/contextual-bandits
content.type: link
content.type: new_line
content.raw: PyPi: 
content.type: text
content.value: PyPi: 
content.href: https://pypi.org/project/contextual-bandits-algos/
content.raw: https://pypi.org/project/contextual-bandits-algos/
content.type: link
content.type: new_line
content.type: new_line
content.raw: 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.
content.type: text
content.value: 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.
rawContent: 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.
reactions.reaction: 🚀
reactions.users: nwaeneh John6666 AtAndDev louisbrulenaudet nicolay-r