TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

Materials for the Practical Sessions of the Reinforcement Learning Summer School 2019: Bandits, RL & Deep RL (PyTorch).

A lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

Another A/B test library

lightweight contextual bandit library for ts/js

Code associated with the NeurIPS19 paper "Weighted Linear Bandits in Non-Stationary Environments"

🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"

Thompson Sampling for Bandits using UCB policy

Deep contextual bandits in PyTorch: Neural Bandits, Neural Linear, and Linear Full Posterior Sampling with comprehensive benchmarking on synthetic and real datasets

Python library of bandits and RL agents in different real-world environments

Python implementation of common RL algorithms using OpenAI gym environments

Code for our PRICAI 2022 paper: "Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior".

Collaborative project for documenting ML/DS learnings.

Multi-armed Bandit Gymnasium Environment

Code for our ICDMW 2018 paper: "Contextual Bandit with Adaptive Feature Extraction".

Deep Reinforcement Learning Agents in Pytorch in a modular framework

C++ implementation of Multi-Armed Bandits (Gaussian and Bernoulli)

Code for our paper "Bandits with Preference Feedback: A Stackelberg Game Perspective"

Code for our AJCAI 2020 paper: "Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward".

This project provides a simulation of multi-armed bandit problems. This implementation is based on the below paper. https://arxiv.org/abs/2308.14350.