Metadata-Version: 2.1
Name: buffalo_gym
Version: 0.0.1
Summary: Buffalo Gym environment
Author: foreverska
Project-URL: Github:, https://github.com/foreverska/buffalo-gym
Keywords: gymnasium,gym
Description-Content-Type: text/markdown
Requires-Dist: gymnasium >=0.26.0
Requires-Dist: numpy

# Buffalo Gym

A multi-armed bandit (MAB) environment for the gymnasium API.
One-armed Bandit is a reference to slot machines, and Buffalo 
is a reference to one such slot machine that I am fond 
of.  MABs are an excellent playground for theoretical exercise and 
debugging of RL agents as they provide an environment that 
can be reasoned about easily.  It helped me once to step back 
and write an MAB to debug my DQN agent.  But there was a lack 
of native gymnasium environments, so I wrote Buffalo, an easy-to-use 
 environment that it might help someone else.

## Buffalo ("Buffalo-v0")

Default multi-armed bandit environment.  Arm center values 
are drawn from a normal distribution (0, arms).  When an 
arm is pulled, a random value is drawn from a normal 
distribution (0, 1) and added to the chosen arm center 
value.  This is not intended to be challenging for an agent but 
easy for the debugger to reason about.

## Using

Install via pip and import buffalo_gym along with gymnasium.

```
import gymnasium  
import buffalo_gym

env = gym.make("Buffalo-v0")
```
