Question 1

How do I compile BanditLib on Windows or Mac?

Accepted Answer

BanditLib is designed for Linux/GNU C++ and doesn't officially support other platforms. You might need to adapt the build scripts or use compatibility tools, but success isn't guaranteed due to dependencies on waf and specific compilers.

Question 2

BanditLib vs Python libraries like scikit-learn for bandit problems?

Accepted Answer

BanditLib is a C++ library focused on simplicity and independence, ideal for C++ projects or learning algorithms from scratch. scikit-learn offers Python-based bandit implementations with more community support and integration, better for rapid prototyping in data science workflows.

Question 3

How can I add a custom reward distribution in BanditLib?

Accepted Answer

The README doesn't provide explicit instructions, but since it emphasizes extensibility, you'll need to modify the source code by creating new distribution classes similar to the existing binary and normal implementations, which may require C++ expertise.

Question 4

What's the performance overhead for simulating thousands of arms?

Accepted Answer

The library isn't optimized for speed, so for large simulations with thousands of arms, it may be slower than specialized libraries. It's best suited for small to medium-scale experiments where code clarity is prioritized over performance.

Question 5

How do I use the plotting tool to visualize bandit algorithm results?

Accepted Answer

Use the included simpleplot.py script after running simulations; it requires Python and Matplotlib installed. The tool reads output files like out/example1.txt to generate plots, but you may need to customize the script for specific visualizations or data formats.

BanditLib

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