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"

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

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

Thompson Sampling for Bandits using UCB policy

Deterministic decision-intelligence MCP server for AI agents — 17 tools, 21 algorithms (LinUCB, HiGHS LP/MIP, PageRank, Monte Carlo, CMA-ES, conformal). Sub-25ms. Zero LLM cost. AAA on Glama. Field-validated in 12+ OSS projects.

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

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

Python implementation of common RL algorithms using OpenAI gym environments

Collaborative project for documenting ML/DS learnings.

Multi-armed Bandit Gymnasium Environment

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

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

Deep Reinforcement Learning Agents in Pytorch in a modular framework

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

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