Multi-Objective Multi-Armed Bandit
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Updated
Jul 17, 2023 - Python
Multi-Objective Multi-Armed Bandit
This repository contains all of the Reinforcement Learning-related projects I've worked on. The projects are part of the graduate course at the University of Tehran.
A small collection of Bandit algorithms, written in Rust 🦀.
Sending personalized marketing offers (called free play in a casino setting) to players by observing data on their gaming behavior and demographic information
Simulating-the-Multi-Armed-Bandit with 10 arms using algorithms like Greedy, Epsilon-Greedy and UCB.
Code for our AAMAS '25 oral paper, 'Multi-agent Multi-armed Bandits with Minimum Reward Guarantee Fairness'.
This rep contains the projects made for the course "Reinforcement Learning and Dynamic Optimization" at TUC (2024).
📊 Web Ad Optimization using Reinforcement Learning (UCB) 🚀 An interactive Machine Learning application that uses the Upper Confidence Bound (UCB) algorithm to optimize advertisement selection and maximize Click Through Rate (CTR). Demonstrates exploration vs exploitation in real-time using Streamlit.
Which Advertisement is the best fit for our business we can directly get through UCB Algorithm
[Python] 4 multi-armed bandit algorithms are implemented to determine which one can most effectively determine the best website configuration that maximise signups.
Reinforcement learning–based adaptive sampling for Physics-Informed Neural Networks using a UCB multi-armed bandit strategy, applied to the 1D heat equation.
An implementation of Bandit Algorithms, focusing on the case of Bernoulli Rewards.
This repository contains several implementations of multi-armed bandit (MAB) agents applied to a simulated cricket match where an agent selects among different strategies with the goal of maximizing runs while minimizing the risk of getting out.
2025 Master's course project – Online Learning Application: codebase, docs, and deliverables.
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