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

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

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

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

Thompson Sampling for Bandits using UCB policy

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".

Multi-armed Bandit Gymnasium Environment

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

This project provides a simulation of multi-armed bandit problems. This implementation is based on the below paper. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322757.

Code and data for the paper "A Combinatorial Multi-Armed Bandit Approach to Correlation Clustering", DAMI 2023

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

Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.

Implementation of the prophet inequalities

Study the interplay between communication and feedback in a cooperative online learning setting.

Foundations of Intelligent and Learning Agenet

Python package (work in progress) implementing sampling policies (known and new) for (contextual) bandits and valid statistical inference methods