How do I compile BanditLib on Windows or Mac?

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.

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

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.

How can I add a custom reward distribution in BanditLib?

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.

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

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.

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

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 — C++ Multi-Armed Bandit Library

Overview

Multi-armed bandit simulation library

Use Cases

Not Ideal For

Projects requiring high-performance, optimized simulations for large datasets
Cross-platform applications needing support beyond Linux/GNU C++
Scenarios demanding diverse reward distributions beyond binary and normal
Teams seeking extensive documentation or GUI-based tools

Pros & Cons

Pros

Simple and Extensible

Prioritizes clarity and ease of modification over optimization, as stated in the README, making it ideal for educational use and experimental prototyping.

No External Dependencies

Requires only a modern C++ compiler and included tools like waf and cmdline.h, ensuring straightforward setup without library conflicts, per the README.

Multiple Algorithm Implementations

Implements key policies such as DMED, KL-UCB, Thompson sampling, and UCB variants, providing a broad range of bandit strategies for experimentation.

Built-in Plotting Utility

Includes a Python/Matplotlib-based plot tool for visualizing simulation results, adding convenience for analysis without additional setup.

Cons

Limited Platform Compatibility

Explicitly only supports Linux/GNU C++ environments and does not check for Windows or MacOS, restricting usability for cross-platform projects.

Not Performance-Optimized

The README admits it's 'not optimized for speed,' making it inefficient for large-scale or real-time simulations compared to optimized libraries.

Minimal Documentation

Beyond the basic README, there's no detailed API documentation or tutorials, which can hinder learning, extension, and troubleshooting.

BanditLib

Overview

Use Cases

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

BanditLib

Overview

Use Cases

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?