File Exchange

image thumbnail

Multi-Armed Bandit Problem Example

version 1.0.1 (154 KB) by Toshiaki Takeuchi
Learn how to implement two basic but powerful strategies to solve multi-armed bandit problems with MATLAB.

8 Downloads

Updated 10 Jan 2019

View License

Casino slot machines have a playful nickname - "one-armed bandit" - because of the single lever it has and our tendency to lose money when we play them.
Ordinary slot machines have only one lever. What if you had multiple levers to pull, each with different payout. This is a multi-armed bandit. You don't know which lever has the highest payout - you just have to try different levers to see which one works best, but for how long? If you keep pulling the low payout lever, you forego more rewards, but you won't know which lever is good until you try sufficient number of times.

Bandit algorithms are related to the field of machine learning called reinforcement learning. Rather than learning from explicit training data, or discovering patterns in static data, reinforcement learning discovers the best option from trial and error with live examples. The multi-armed bandits focus on the question of exploration vs. exploitation trade-off - how much resources should be spent in trial and error vs. maximizing the benefit. There are many different formulation of bandit problems and strategies to solve them.

Cite As

Toshiaki Takeuchi (2019). Multi-Armed Bandit Problem Example (https://www.mathworks.com/matlabcentral/fileexchange/69598-multi-armed-bandit-problem-example), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (0)

Updates

1.0.1

Added an image

MATLAB Release Compatibility
Created with R2018b
Compatible with R2018b to any release
Platform Compatibility
Windows macOS Linux