Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
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Updated
May 2, 2023 - Python
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
A simple, extensible library for developing AutoML systems
👤 Multi-Armed Bandit Algorithms Library (MAB) 👮
Python application to setup and run streaming (contextual) bandit experiments.
Simple implementation of the CGP-UCB algorithm.
Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions
Offline evaluation of multi-armed bandit algorithms
Multi-armed bandit algorithm with tensorflow and 11 policies
Author's implementation of the paper Correlated Age-of-Information Bandits.
Implementation of the X-armed Bandits algorithm, as detailed in the paper, "X-armed Bandits", Bubeck et al., 2011.
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
Implementations of the bandit algorithms with unordered and ordered slates that are described in the paper "Non-Stochastic Bandit Slate Problems", by Kale et al. 2010.
Contextual Multi-Armed Bandit Reward Tracker & Model Trainer
Multi-Player Bandits Revisited [L. Besson & É. Kaufmann]
Multi-Armed Bandit method of accurately estimating the largest parameter out of a set of candidates.
Code template for multi-armed bandit algorithm
The GitHub repository for "Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo", AISTATS 2024.
Data Intelligence Application: Pricing and Advertising learning strategies
Python implementation for Reinforcement Learning algorithms -- Bandit algorithms, MDP, Dynamic Programming (value/policy iteration), Model-free Control (off-policy Monte Carlo, Q-learning)
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