Metadata-Version: 2.1
Name: mabby
Version: 0.1.2
Summary: A multi-armed bandit (MAB) simulation library
License: Apache-2.0
Author: Ethan Wu
Author-email: ew2664@columbia.edu
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: matplotlib (>=3.7.0,<4.0.0)
Requires-Dist: numpy (>=1.24.2,<2.0.0)
Requires-Dist: overrides (>=7.3.1,<8.0.0)
Description-Content-Type: text/markdown

<h1 align="center">
<img src="https://raw.githubusercontent.com/ew2664/mabby/main/assets/mabby-logo-title.png" width="500">
</h1>

[![PyPI](https://img.shields.io/pypi/v/mabby)](https://pypi.org/project/mabby/)
[![license](https://img.shields.io/github/license/ew2664/mabby)](https://github.com/ew2664/mabby/blob/main/LICENSE)
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**mabby** is a library for simulating [multi-armed bandits (MABs)](https://en.wikipedia.org/wiki/Multi-armed_bandit), a resource-allocation problem and framework in reinforcement learning. It allows users to quickly yet flexibly define and run bandit simulations, with the ability to:

- choose from a wide range of classic bandit algorithms to use
- configure environments with custom arm spaces and rewards distributions
- collect and visualize simulation metrics like regret and optimality

## Installation

Prerequisites: [Python 3.9+](https://www.python.org/downloads/) and `pip`

Install **mabby** with `pip`:

```bash
pip install mabby
```

## Basic Usage

The code example below demonstrates the basic steps of running a simulation with **mabby**. For more in-depth examples, please see the [Usage Examples](https://ew2664.github.io/mabby/examples/) section of the **mabby** documentation.

```python
import mabby as mb

# configure bandit arms
bandit = mb.BernoulliArm.bandit(p=[0.3, 0.6])

# configure bandit strategy
strategy = mb.strategies.EpsilonGreedyStrategy(eps=0.2)

# setup simulation
simulation = mb.Simulation(bandit=bandit, strategies=[strategy])

# run simulation
stats = simulation.run(trials=100, steps=300)

# plot regret statistics
stats.plot_regret()
```

## Contributing

Please see [CONTRIBUTING](https://ew2664.github.io/mabby/contributing/) for more information.

## License

This software is licensed under the Apache 2.0 license. Please see [LICENSE](https://ew2664.github.io/mabby/license/) for more information.

