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contextual: Simulation and Analysis of Contextual Multi-Armed Bandit Policies
Facilitates the simulation and evaluation of context-free and contextual multi-Armed Bandit policies or algorithms to ease the implementation, evaluation, and dissemination of both existing and new bandit algorithms and policies.
Getting started
README.md
Demo: Bandits, Propensity Weighting & Simpson's Paradox in R"
Demo: Basic Epsilon Greedy"
Demo: Basic Synthetic cMAB Policies
Demo: MAB Policies Comparison
Demo: MAB Replication Eckles & Kaptein (Bootstrap Thompson Sampling)
Demo: MovieLens 10M Dataset"
Demo: Offline cMAB: CarsKit DePaul Movie Dataset"
Demo: Offline cMAB LinUCB evaluation"
Demo: Replication of John Myles White, Bandit Algorithms for Website Optimization"
Demo: Replication Sutton & Barto, Reinforcement Learning: An Introduction, Chapter 2
Getting started: running simulations"
Offline evaluation: Replication of Li et al 2010
Browse package contents
Package details |
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| Author | Robin van Emden [aut, cre] (<https://orcid.org/0000-0001-5820-8638>), Maurits Kaptein [ctb] (<https://orcid.org/0000-0002-6316-7524>) |
| Maintainer | Robin van Emden <robinvanemden@gmail.com> |
| License | GPL-3 |
| Version | 0.9.8.4 |
| URL | https://github.com/Nth-iteration-labs/contextual |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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