Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
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Updated
May 2, 2023 - Python
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
Papers about recommendation systems that I am interested in
🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on
A simple, extensible library for developing AutoML systems
Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
👤 Multi-Armed Bandit Algorithms Library (MAB) 👮
Demo project using multi-armed bandit algorithm
Python application to setup and run streaming (contextual) bandit experiments.
More about the exploration-exploitation tradeoff with harder bandits
COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdit) - and strategies for HCS context
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
En este proyecto de GitHhub podrás encontrar parte del material que utilizo para impartir las clases del módulo introductorio de Reinforcement Learning (Aprendizaje por Refuerzo)
Simple implementation of the CGP-UCB algorithm.
Software for the experiments reported in the RecSys 2019 paper "Multi-Armed Recommender System Bandit Ensembles"
Simple A/B testing library for Clojure
CUNYBot, an AI that plays complete games of Starcraft.
Offline evaluation of multi-armed bandit algorithms
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