Another A/B test library
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Updated
May 22, 2024 - Scala
Another A/B test library
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
TJHSST Artificial Intelligence Labs from the 2022-23 School Year with Dr. Gabor
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
lightweight contextual bandit library for ts/js
An exploration of multi-armed Bernoulli bandits in reinforcement learning, complete with experiments and observations.
This project provides a simulation of multi-armed bandit problems. This implementation is based on the below paper. https://arxiv.org/abs/2308.14350.
Code and data for the paper "A Combinatorial Multi-Armed Bandit Approach to Correlation Clustering", DAMI 2023
Implementation of Multi-Armed Bandit (MAB) algorithms UCB and Epsilon-Greedy. MAB is a class of problems in reinforcement learning where an agent learns to choose actions from a set of arms, each associated with an unknown reward distribution. UCB and Epsilon-Greedy are popular algorithms for solving MAB problems.
Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.
Code for our PRICAI 2022 paper: "Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior".
Coursework, Stochastic Models and Optimization, BSE, Term 3, Class of 2022
Collaborative project for documenting ML/DS learnings.
A two armed bandit simulation and comparison with theoritical convergence
Deep Reinforcement Learning Agents in Pytorch in a modular framework
Python library of bandits and RL agents in different real-world environments
Implementation of the experiments for "Cooperative Online Learning with Feedback Graphs" Cesa-Bianchi, Cesari, Della Vecchia (https://arxiv.org/abs/2106.04982)
A benchmark to test decision-making algorithms for contextual-bandits. The library implements a variety of algorithms (many of them based on approximate Bayesian Neural Networks and Thompson sampling), and a number of real and syntethic data problems exhibiting a diverse set of properties.
Implementation of the prophet inequalities
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