TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
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Updated
Mar 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
Collaborative project for documenting ML/DS learnings.
Python library of bandits and RL agents in different real-world environments
Thompson Sampling for Bandits using UCB policy
Play Rock, Paper, Scissors (Kaggle competition) with Reinforcement Learning: bandits, tabular Q-learning and PPO with LSTM.
An assignment for the implementation of Online Learning, Bandits and Reinforcement Learning
Code for our ICDMW 2018 paper: "Contextual Bandit with Adaptive Feature Extraction".
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.
Code associated with the NeurIPS19 paper "Weighted Linear Bandits in Non-Stationary Environments"
A python library for (finite) Partial Monitoring algorithms
This repo contains all the stuff I encountered while playing OverTheWire games.
Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.
Simple Implementations of Bandit Algorithms in python
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