buffalo-gym 0.0.1

Buffalo Gym environment

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")