There are 0 repository under bandits topic.
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).
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
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
Collaborative project for documenting ML/DS learnings.
Deep Reinforcement Learning Agents in Pytorch in a modular framework
Simple Implementations of Bandit Algorithms in python
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.
Play Rock, Paper, Scissors (Kaggle competition) with Reinforcement Learning: bandits, tabular Q-learning and PPO with LSTM.
A two armed bandit simulation and comparison with theoritical convergence
Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.
An exploration of multi-armed Bernoulli bandits in reinforcement learning, complete with experiments and observations.
Foundations of Intelligent and Learning Agenet
Implementation of the prophet inequalities
This repo contains all the stuff I encountered while playing OverTheWire games.
TJHSST Artificial Intelligence Labs from the 2022-23 School Year with Dr. Gabor
Implementation of the experiments for "Cooperative Online Learning with Feedback Graphs" Cesa-Bianchi, Cesari, Della Vecchia (https://arxiv.org/abs/2106.04982)
An assignment for the implementation of Online Learning, Bandits and Reinforcement Learning
Implementation of Multi-Armed Bandit (MAB) algorithms UCB and Epsilon-Greedy. MAB is a class of problems in reinforcement learning where an agent learns to choose actions from a set of arms, each associated with an unknown reward distribution. UCB and Epsilon-Greedy are popular algorithms for solving MAB problems.
Coursework, Stochastic Models and Optimization, BSE, Term 3, Class of 2022