Multi-Armed Bandit (MAB) algorithm implementation in go
-
Updated
Nov 25, 2019 - Go
Multi-Armed Bandit (MAB) algorithm implementation in go
Implementations of basic concepts dealt under the Reinforcement Learning umbrella. This project is collection of assignments in CS747: Foundations of Intelligent and Learning Agents (Autumn 2017) at IIT Bombay
Reversi (Othello) AI game in C#. Using Monte Carlo Tree Search algorithm AND BTMM algorithm.
This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.
An implementation of solvers for the multi-armed-bandit-problem in JavaScript.
This repository is focused on my assignments solutions for the Statistical Techniques for Data Science course at Innopolis University.
Python implementation of Monte Carlo Tree Search
Pricing and Social Influence Maximization using Reinforcement Learning algorithms in Data Intelligence Applications projects from Politechnic of Milan
My reports for the reinforcement learning class given at the ENS
Reinforcement learning techniques applied to solve pricing problems in e-commerce applications. Final project for "Online learning applications" course (2021-2022)
REST service, that returns content sorted by UCB1 algorithm.(Multi-Armed Bandit algorithm). Spring Boot, Kotlin
Add a description, image, and links to the ucb1 topic page so that developers can more easily learn about it.
To associate your repository with the ucb1 topic, visit your repo's landing page and select "manage topics."