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
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.
An Implementation of the N-Tuple Bandits Evolutionary Algorithm.
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.
AI Reinforcement Learning in Python
Ads Click-through rate using thompson sampling
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