reinforced-lib 0.1.2

Reinforcement learning library

Reinforced-lib: Reinforcement learning library

Overview

Reinforced-lib is a Python library designed to support research and prototyping using Reinforced Learning (RL) algorithms. The library can serve as a simple solution with ready to use RL workflows, as well as an expandable framework with programmable behaviour. Thanks to a functional implementation of the library's core, we can provide full access to JAX’s jit functionality, which boosts the agents performance significantly.

Installation

You can install the latest released version of Reinforced-lib from PyPI via:

pip install reinforced-lib

You can also download source code and install the development dependencies if you want to build the documentation locally:

git clone [e-mail]:m-wojnar/reinforced-lib.git
cd reinforced-lib
pip3 install ".[dev]"

Example code

import gym

import reinforced_lib as rfl
from reinforced_lib.agents import ThompsonSampling
from reinforced_lib.exts import IEEE_802_11_ax

rlib = rfl.RLib(
    agent_type=ThompsonSampling,
    ext_type=IEEE_802_11_ax
)

env = gym.make('WifiSimulator-v1')

state = env.reset()
done = False

while not done:
    action = rlib.sample(**state)
    state, reward, done, info = env.step(action)

Integrated IEEE 802.11ax support

Library design is distinctly influenced by the desire to support research in Wi-Fi. It can be a tool for researchers to optimize the Wi-Fi protocols with built-in RL algorithms and provided IEEE 802.11ax environment extension.

We also provide simple ns-3 simulation and RL-based rate adaptation manager for the IEEE 802.11ax standard in examples.

Citing Reinforced-lib

To cite this repository:

@software{reinforcedlib2022,
  author = {Maksymilian Wojnar and Wojciech Ciężobka},
  title = {{R}einforced-lib: {R}einforcement learning library},
  url = {http://github.com/m-wojnar/reinforced-lib},
  year = {2022},
}