TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
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Updated
Jul 25, 2024 - Python
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.
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".
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
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.
Play Rock, Paper, Scissors (Kaggle competition) with Reinforcement Learning: bandits, tabular Q-learning and PPO with LSTM.
Code and data for the paper "A Combinatorial Multi-Armed Bandit Approach to Correlation Clustering", DAMI 2023
Foundations of Intelligent and Learning Agenet
Implementation of the prophet inequalities
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.
Study the interplay between communication and feedback in a cooperative online learning setting.
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