Python utilities to compute a lower bound of the expected sample complexity to identify the best arm in a bandit model
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Updated
Sep 8, 2021 - Python
Python utilities to compute a lower bound of the expected sample complexity to identify the best arm in a bandit model
Randomized Greedy Learning Under Full-bandit Feedback
Repository contains codes for the course CS780: Deep Reinforcement Learning
🎩🤠Some Bandit Algorithms in Typescript
2024 ICML Official code
An Implementation of the N-Tuple Bandits Evolutionary Algorithm.
Code repository for the paper No-Regret Approximate Inference via Bayesian Optimisation, published at UAI 2021
An implementation of the TME from the Reinforcement Learning course given at Sorbonne University.
Creation of filters using electric passive elements
Implementation of greedy, ε-greedy and softmax methods for n-armed bandit problem
An implementation of the matching bandit algorithm in http://proceedings.mlr.press/v139/sentenac21a.html.
Today I Learned - Reinforcement Learning
Several multi-armed bandit strategies with additional holding option for smoother exploration.
a collection of google colab notebooks with educational stuff about bandits and their variations
A open source multi arm bandit framework for optimize your website quickly. You’ll quickly use the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through this framework written in Java, which you can easily adapt for deployment on your own website.
This repo contains code for multi-armed bandit algorithm testing and local multiplayer competition.
Ads Click-through rate using thompson sampling
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