# Tsetlin Machine for Solving Contextual Bandit Problems

arXiv: https://arxiv.org/abs/2202.01914 · HTML: https://mlsanity.com/paper/2202.01914/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [A Contextual Bandit Bake-off](https://mlsanity.com/paper/1802.04064/) (2018)
- [A Neural Networks Committee for the Contextual Bandit Problem](https://mlsanity.com/paper/1409.8191/) (2014)
- [On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators](https://mlsanity.com/paper/2007.14268/) (2020)
- [Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling](https://mlsanity.com/paper/2009.04861/) (2020)
- [Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability](https://mlsanity.com/paper/2005.05131/) (2020)
- [Random Forest for the Contextual Bandit Problem - extended version](https://mlsanity.com/paper/1504.06952/) (2015)
- [Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine](https://mlsanity.com/paper/2105.14506/) (2021)
- [Contextual Bandits for adapting to changing User preferences over time](https://mlsanity.com/paper/2009.10073/) (2020)

## Built on by (most cited first, 10 of 10)

- [Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size](https://mlsanity.com/paper/2301.08190/) (2023)
- [Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification](https://mlsanity.com/paper/2301.10181/) (2023)
- [Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses](https://mlsanity.com/paper/2501.19018/) (2025)
- [The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs](https://mlsanity.com/paper/2507.14874/) (2025)
- [Contracting Tsetlin Machine with Absorbing Automata](https://mlsanity.com/paper/2310.11481/) (2023)
- [Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space](https://mlsanity.com/paper/2505.16386/) (2025)
- [Uncertainty Quantification in the Tsetlin Machine](https://mlsanity.com/paper/2507.04175/) (2025)
- [Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning](https://mlsanity.com/paper/2310.02005/) (2023)
- [A Methodology for Transparent Logic-Based Classification Using a Multi-Task Convolutional Tsetlin Machine](https://mlsanity.com/paper/2510.01906/) (2025)
- [Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space](https://mlsanity.com/paper/2511.19273/) (2025)

## Abstract (arXiv)

This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed bandit learning algorithm relies on straightforward bit manipulation, thus simplifying computation and interpretation. We then present a mechanism for performing Thompson sampling with Tsetlin Machine, given its non-parametric nature. Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular base learners on eight out of nine datasets. We further analyze the interpretability of our learner, investigating how arms are selected based on propositional expressions that model the context.