TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
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Updated
Aug 20, 2024 - Python
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 implementation of common RL algorithms using OpenAI gym environments
Python library of bandits and RL agents in different real-world environments
Code for our PRICAI 2022 paper: "Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior".
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".
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
Simple Implementations of Bandit Algorithms in python
Code for our AJCAI 2020 paper: "Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward".
A python library for (finite) Partial Monitoring algorithms
This project provides a simulation of multi-armed bandit problems. This implementation is based on the below paper. https://arxiv.org/abs/2308.14350.
Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.
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