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

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

lightweight contextual bandit library for ts/js

Thompson Sampling for Bandits using UCB policy

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.

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".

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

Deep Reinforcement Learning Agents in Pytorch in a modular framework

Collaborative project for documenting ML/DS learnings.

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

Simple Implementations of Bandit Algorithms in python

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.

A python library for (finite) Partial Monitoring algorithms

Play Rock, Paper, Scissors (Kaggle competition) with Reinforcement Learning: bandits, tabular Q-learning and PPO with LSTM.